Resources

BEL-Based AI Models

2019

Wavelet Fuzzy Brain Emotional Learning Control System Design for MIMO Uncertain Nonlinear Systems (2019) [link]
This paper aims to present a novel efficient scheme in order to more effectively control the multiple input and multiple output (MIMO) uncertain nonlinear systems. A wavelet fuzzy brain emotional learning controller (WFBELC) model is proposed, which is comprises the benefit of wavelet function, fuzzy theory and brain emotional neural network. When it is used as the main tracking controller for a MIMO uncertain nonlinear systems, the performances of the system, such as the approximation ability, the learning performance and the convergence rate, will be effectively improved. Meanwhile, the gradient descent method is used to adjust the parameters online of WFBELC and the Lyapunov function is employed to guarantee the rapid convergence of the control systems. For the sake of the further illustrating the superiority of this model, two examples of uncertain nonlinear systems, a Duffing-Holmes chaotic system and a Chua’s chaotic circuit, are studied. After compared with other models, the test results show that the proposed model can be applied to obtain more satisfactory control performance and be more suitable to deal with the influence of the uncertainty of the MIMO nonlinear systems.

2018

An introduction to brain emotional learning inspired models (BELiMs) with an example of BELiMs’ applications (2018) [link
Brain emotional learning-inspired models (BELiMs) is a new category of computational intelligence (CI) paradigms. The general structure of BELiMs is based on the neural structure of the emotion system which processes and evaluates fear-induced stimuli, to produce emotional responses. The function of a BELiM is implemented by assigning adaptive networks to different parts of its structure. The primary motivation for developing BELiMs is to address model and time complexity issues associated with supervised machine learning artificial neural networks and neuro-fuzzy methods. One of the applications of BELiMs is chaotic time series prediction problems. A BEliM can be used as a time series prediction model. This paper introduces BELiMs as a new CI paradigm and explains historical, theoretical, structural and functional aspects of BELiMs. I also validate and evaluate the performance of BELiMs as a time series prediction model by examining different variations of BELiMs on benchmark time series data sets and comparing obtained results with different CI models.

Self-organizing Brain Emotional Learning Controller Network for Intelligent Control System of Mobile Robots (2018) [link]
The trajectory tracking ability of mobile robots suffers from uncertain disturbances. This paper proposes an adaptive control system consisting of a new type of self-organizing neural network controller for mobile robot control. The newly designed neural network contains the key mechanisms of a typical brain emotional learning controller network and a self-organizing radial basis function network. In this system, the input values are delivered to a sensory channel and an emotional channel; and the two channels interact with each other to generate the final outputs of the proposed network. The proposed network possesses the ability of online generation and elimination of fuzzy rules to achieve an optimal neural structure. The parameters of the proposed network are on-line tunable by the brain emotional learning rules and gradient descent method; in addition, the stability analysis theory is used to guarantee the convergence of the proposed controller. In the experimentation, a simulated mobile robot was applied to verify the feasibility and effectiveness of the proposed control system. The comparative study using the cutting-edge neural network-based control systems confirms the proposed network is capable of producing better control performances with high computational efficiency.

Biologically-Inspired Intelligent Flocking Control for Networked Multi-UAS with Uncertain Network Imperfections (2018) [link]
In this paper, a biologically-inspired distributed intelligent control methodology is proposed to overcome the challenges, i.e., networked imperfections and uncertainty from the environment and system, in networked multi-Unmanned Aircraft Systems (UAS) flocking. The proposed method is adopted based on the emotional learning phenomenon in the mammalian limbic system, considering the limited computational ability in the practical onboard controller. The learning capability and low computational complexity of the proposed technique make it a propitious tool for implementing in real-time networked multi-UAS flocking considering the network imperfection and uncertainty from environment and system. Computer-aid numerical results of the implementation of the proposed methodology demonstrate the effectiveness of this algorithm for distributed intelligent flocking control of networked multi-UAS.

Evolutionary-Based BEL Controller Applied to a Magneto-Rheological Structural System (2018) [link]

This work addresses the problem of finding the best controller parameters in order to improve the response of a single degree-of-freedom structural system under earthquake excitation. The control paradigm considered is based on brain emotional learning (BEL) and the actuation over the building dynamics is carried out by changing the stiffness of a magneto-rheological damper. A typical BEL-based controller requires the definition of several parameters which can prove difficult and non-intuitive to obtain. For this reason, an evolutionary-based search technique has been added to the current problem framework in order to automate the controller design. In particular, the particle swarm optimization method is chosen as the evolutionary based technique to be integrated within the current control paradigm. The obtained results suggest that, indeed, it is possible to parametrize a BEL controller using an evolutionary-based algorithm. Moreover, a simulation shows that the obtained results can outperform the ones obtained by manual tuning each controller parameter individually.

A biological brain-inspired fuzzy neural network: Fuzzy emotional neural network (2018) [link]
In this paper, a brain-inspired fuzzy emotional neural network (FUZZ-ENN) is proposed for uncertainty prediction tasks in real world applications. In the proposed FUZZ-ENN, amygdala connections are modeled by fuzzy IF-THEN behavioral rules and orbitofrontal module inhibits the amygdala responses in order to decrease the uncertainty. This computational model is based on the inhibitory connections in the human emotional brain’s nervous system inhibiting the uncertainty. In this paper, genetic algorithm is applied for optimal tuning of crisp numerical and fuzzy parameters of the proposed model. A traditional neural model and a two layered emotional neural network (ENN) are also implemented for comparison purposes on the electrical load and wind power forecasting problem and the prediction of geomagnetic activity indices as two real world case studies. Numerical results indicate the superiority of the proposed approach in term of lower uncertainty in the prediction.

Intelligent Speed Control of Hybrid Stepper Motor Considering Model Uncertainty Using Brain Emotional Learning (2018) [link]
This paper presents an implementation of the brain emotional learning-based intelligent controller (BELBIC) for precise speed tracking of the hybrid stepper motor (HSM). Such a configuration is applicable where high resolution and accuracy is essential particularly in uncertain conditions. The proposed controller is a model-free controller independent of the model dynamics and variations that occur in a system. It is capable of autolearning to handle unforeseen disturbances. To evaluate the performance of the BELBIC controller in realistic conditions, the uncertainty of the system as a result of mechanical parameter variation and load torque disturbance is considered. To verify an excellent dynamic performance and the feasibility of the BELBIC, the system is simulated in MATLAB Simulink, and the results of the simulation are compared with an optimized proportional integral (PI) controller. The simulation results confirm the superior performance of the BELBIC for fast and precise speed response as well as its potential in dealing with nonlinearity and uncertainty handling as compared with that of the PI controller. The proposed controller is used in realistic applications, such as tunable-laser system and robot-assisted surgery.

Implementation of artificial intelligence based optimally tuned controllers to a class of embedded nonlinear system (2018) [link]
This paper concerns with the application of the Bat Inspired Algorithm (BIA) as an optimization technique to find the optimal parameters of two classes of controllers. The first is the classical Proportional-Integral-Derivative (PID). The second is the hybrid fractional order and Brain Emotional Intelligent controller. The two controllers have been applied, separately, for the load frequency control of a single area electric power system with three physical imbedded nonlinearities. The first nonlinearity is owing to the generation’s rate constraint (GRC). The second is due to the governor dead band (GDB). The last is due to the time delay imposed by the governor-turbine link, the thermodynamic process, and the communication channels. These nonlinearities have been embedded in the simulation model of the system under study. Matlab/Simulink software has been applied to obtain the results of applying the two classes of controllers which have been, optimally, tuned using the BIA. The Integral of Square Error (ISE) criterion has been chosen as a component of the objective function beside the percentage overshoot and settling time for the optimum tuning technique of the two controllers. The simulation results show that when using the hybrid fractional order and Brain Emotional Intelligent controller, it gives better response and performance indices than the conventional Proportional-Integral-Derivative (PID) controllers.

Brain Emotional Learning-inspired Models (BELiMs) for Affective Computing Applications (2018) [link]
The goal of this project is to apply Brain Emotional Learning-inspired Models (BELiMs) for Affective Computing Applications

2017

Applying Fuzzy Mathematical Model of Emotional Learning for EEG Signal Classification Between Schizophrenics and Control Participant (2017) [link]
This paper concerns the diagnosis of schizophrenia using an electroencephalogram signals, and introduces a new framework based on the brain emotional learning model that can be applied in wide varieties of articial intelligence applications. We propose the extended supervised version of the neuro-based computational model of an emotional learning referred as to the decay brain emotional learning based fuzzy inference system (DBELFIS). This architecture is based on fuzzy inference system, and it is build-up from the fusion algorithm based on brain emotional learning and fuzzy inference system. In this paper, we compared the proposed method with Multi-layer Perceptron (MLP), brain emotional learning (BEL) and a limbic based articial emotional neural network (LiAENN). Substantial experimental results show that the proposed approach can effectively diagnose schizophrenia.

A modified brain emotional learning model for earthquake magnitude and fear prediction (2017) [link]
Brain emotional learning (BEL) model has been used frequently for predicting a quantity or modeling complex and nonlinear systems in recent years. In this research, two methods proposed for improving the efficiency of original BEL model using fuzzy rules, learning automata concepts and optimization algorithms. In the first proposed method, different optimization algorithms and continuous action-set learning automata (CALA) were used for finding the weights of BEL model, while in the second proposed model, the weights obtained using original rules of BEL model. In fact, in the second model finite action-set learning automata, CALA and different optimization algorithms were used for calibrating the learning parameters of the model. Also in the both proposed methods after extracting frequency features in thalamus, deep belief network is used in the sensory cortex for reducing the size of features. In addition, ANFIS is used for making fuzzy rules in the amygdala. The proposed models were used for magnitude and consequently fear prediction of the earthquakes. The results show that although both proposed methods are more accurate than the original BEL model and could be used successfully, the second proposed model is more precise and reliable than the first proposed model.

An Introduction to Brain Emotional Learning inspired Models (BELiMs) with an Example of BELiMs’ Application (2017) [link]
Brain Emotional Learning-inspired Models(BELiMs)can be viewed as a new category of computational intelligence (CI) paradigms. The general structure of BELiMs is based on the neural structure of the emotion system which processes and evaluates fear-induced stimuli, to produce emotional responses. The function of a BELiM is implemented by assigning adaptive networks to different parts of the general structure. The primary motivation for developing BELiMs is to address model and time complexity issues associated with artificial neural networks and neuro-fuzzy methods. BELiMs are categorized as machine learning tools and could be utilized for chaotic time series prediction and have outperformed the performance of the conventional CI models for this type of problem. This paper explains historical, theoretical, structural and functional aspects of BELiMs. It also presents the obtained results from BELiMs as time series prediction models.

Brain Emotional Learning Based Intelligent Decoupler for Nonlinear Multi-Input Multi-Output Distillation Columns (2017) [link]
The distillation process is vital in many fields of chemical industries, such as the two-coupled distillation columns that are usually highly nonlinear Multi-Input Multi-Output (MIMO) coupled processes. The control of MIMO process is usually implemented via a decentralized approach using a set of Single-Input Single-Output (SISO) loop controllers. Decoupling the MIMO process into group of single loops requires proper input-output pairing and development of decoupling compensator unit. This paper proposes a novel intelligent decoupling approach for MIMO processes based on new MIMO brain emotional learning architecture. A MIMO architecture of Brain Emotional Learning Based Intelligent Controller (BELBIC) is developed and applied as a decoupler for 4 input/4 output highly nonlinear coupled distillation columns process. Moreover, the performance of the proposed Brain Emotional Learning Based Intelligent Decoupler (BELBID) is enhanced using Particle Swarm Optimization (PSO) technique. The performance is compared with the PSO optimized steady state decoupling compensation matrix. Mathematical models of the distillation columns and the decouplers are built and tested in simulation environment by applying the same inputs. The results prove remarkable success of the BELBID in minimizing the loops interactions without degrading the output that every input has been paired with.

Brain emotional limbic-based intelligent controller design for control of a haptic device (2017) [link]
This paper proposes a new decision method for defining learning rates that automatically modulates those learning rates depending on the control situation. To show the effectiveness of the proposed method, existing simulation results obtained with a BELBIC and the simulation results obtained with the BELBIC proposed in this paper are compared. This paper performs a simulation to determine the potential applications of BELBIC to a haptic device control. A five-bar linkage haptic device with two active joints and two degrees of freedom is used in the simulation. Potential applications are studied by comparing the simulation results from a BELBIC with those from a proportional-integral controller.

Brain Emotional Learning Based Intelligent Controller for Velocity Control of an Electro Hydraulic Servo System (2017) [link]
In this paper, a biologically motivated controller based on mammalian limbic system called Brain Emotional Learning Based Intelligent Controller (BELBIC) is used for velocity control of an Electro Hydraulic Servo System (EHSS) in presence of flow nonlinearities, internal friction and noise. It is shown that this technique can be successfully used to stabilize any chosen operating point of the system with noise and without noise. All derived results are validated by computer simulation of a nonlinear mathematical model of the system. The controllers which introduced have big range for control the system. We compare BELBIC controller results with feedbacks linearization, backstepping and PID controller.

Robust BELBIC-Extension for Trajectory Tracking Control (2017) [link]
In real-life trajectory tracking applications of robotic manipulators uncertain robot dynamics, external disturbances and switching constraints which cannot be accommodated for by a conventional controller affect the system performance. We suggested an additional control element combining sliding mode and bio-mimetic, neurologically-inspired BELBIC (brain emotional learning-based intelligent control). The former is invariant to internal and external uncertainties and guarantees robust behavior. The latter is based on an interplay of inputs relating to environmental information through error-signals of position and sliding surfaces and of emotional signals regulating the learning rate and adapting the future behaviour based on prior experiences and with the goal to maximize a reward function. We proofed the stability and the performance of the suggested control scheme through Lyapunov theory and numerical simulations, respectively.

Modeling and implementation of brain emotional controller for Permanent Magnet Synchronous motor drive (2017) [link]
This paper presents a bio-inspired emotion based intelligent controller for Permanent Magnet Synchronous Machine (PMSM) drive. There are certain parts in mammal brain are responsible to generate emotions, which can be used as controller, namely brain emotional controller. The evolutionary development of brain emotional control technique is used in many applications to control nonlinearities in the system. The performance of emotional controller is analyzed by considering control of speed and stator currents harmonics of PMSM drive at different operating conditions. The performance of the controller is compared with existing controllers in offline simulations and in real-time Hardware-in-loop (HIL) simulations using OPAL-RT simulator. Display Omitted We presented modified sensory signal and emotional cue functions.Proposed brain emotional controller tested on permanent magnet synchronous motor drive.Proposed controller results are compared with PI and Fuzzy logic controllers.Simulation results validated with real time implementation using Opal-RT simulator.

Teleoperation of Pneumatic Actuators: Design, Experimental Evaluation, and Stability Analysis (2017) [link]
Pneumatic systems are inexpensive, safe and require low maintenance. Because of the actuation material, air, they are potential candidates for dealing with external force. Teleoperation of pneumatic actuators can be very beneficial in applications in which the remote actuator needs to interact with the external force, e.g. telerehabilitation.
This thesis focuses on the teleoperation of a low-cost solenoid-driven pneumatic actuator. Firstly, a novel intelligent position controller is applied and experimented with on the pneumatic actuator and is later compared to two other controllers. The best among the three is chosen for the rest of the thesis. A unilateral pneumatic teleoperation system employing an electrically-actuated joystick is successfully developed and evaluated using impedance and admittance control schemes for dealing with the external force. Stability analysis is assured for autonomous and non-autonomous systems using the concept of Lyapunov Exponents (LEs). The concept of LEs allows the stability analysis of an available system, and as a result, it does not impose any limitations on the system parameters. In addition, it can show the effect of changing a certain parameter on the stability. Using this concept, the effect of changing a few parameters on stability is studied. The performances of admittance and impedance unilateral systems are then compared in terms of positioning accuracy, energy dissipation and fast response to the external force. It is shown that admittance unilateral teleoperation offers higher positioning accuracy and damping characteristics and reacts faster to the external force.
For the first time, a bilateral teleoperation system is applied to a solenoid valve-driven pneumatic actuator using an electrically-actuated haptic device. Unlike the last two methods, the slave robot does not deal with the external force independently. Instead, this force is rendered on a haptic device and felt by the operator. By changing the admittance of the hand, the operator indirectly deals with the external force. Experimental verification shows the effectiveness of the developed bilateral teleoperation system.

Brain Emotional Learning-Based Intelligent Controller for flocking of Multi-Agent Systems (2017) [link]
A biologically-inspired intelligent controller based on a computational model of emotional learning in mammal’s brain is employed for flocking control of Multi-Agent Systems (MAS). The methodology, known as Brain Emotional Learning Based Intelligent Controller (BELBIC), is implemented in this application for the first time, enhancing the flocking strategy with multi-objective properties. The learning capabilities added by BELBIC to the flocking are very useful, especially when dealing with noises and/or system uncertainty. Furthermore, the low computational complexity of the proposed method makes it very promising for implementation in real-time applications. Numerical results of the BELBIC-based flocking for MAS demonstrate the effectiveness of the proposed approach.

An Improved Brain-Inspired Emotional Learning Algorithm for Fast Classification (2017) [link]
Classification is an important task of machine intelligence in the field of information. The artificial neural network (ANN) is widely used for classification. However, the traditional ANN shows slow training speed, and it is hard to meet the real-time requirement for large-scale applications. In this paper, an improved brain-inspired emotional learning (BEL) algorithm is proposed for fast classification. The BEL algorithm was put forward to mimic the high speed of the emotional learning mechanism in mammalian brain, which has the superior features of fast learning and low computational complexity. To improve the accuracy of BEL in classification, the genetic algorithm (GA) is adopted for optimally tuning the weights and biases of amygdala and orbitofrontal cortex in the BEL neural network. The combinational algorithm named as GA-BEL has been tested on eight University of California at Irvine (UCI) datasets and two well-known databases (Japanese Female Facial Expression, Cohn–Kanade). The comparisons of experiments indicate that the proposed GA-BEL is more accurate than the original BEL algorithm, and it is much faster than the traditional algorithm.

Can wireless sensor networks be emotional? A survey of computational models of emotions and their applications for wireless sensor networks (2017) [link]
Advances in psychology have revealed that emotions and rationality are interlinked and emotions are essential for rational behaviour and decision making. Therefore, integration of emotions with intelligent systems has become an important topic in engineering. The integration of emotions into intelligent systems requires computational models to generate emotions from external and internal sources. This paper first provides a survey of current computational models of emotion and their applications in engineering. Finally, it assesses potential of integrating emotions in wireless sensor networks (WSNs) by listing some use scenarios and by giving one model application. In this model application performance of a neural network for event detection has been improved using brain emotional learning based intelligent controller (BELBIC).

An intelligent method for generating artificial earthquake records based on hybrid PSO-parallel brain emotional learning inspired model (2017) [link]
Simplicity and high speed of brain emotional model made it an effective computational method, which is used in various applications. In this study, a modified model of brain emotional learning is used for generating artificial earthquake records. In fact, in earthquake engineering, strong ground motions are valuable information, which are recorded in each earthquake. These records could be used for linear and nonlinear time-history analysis of structures. Unfortunately, the numbers of recorded strong ground motions are not enough for most areas of the world. Therefore, many seismic codes permit to use artificial records, which contain specific characteristics. Because of the advantages of emotional models, a hybrid PSO–parallel brain emotional learning model is used for generating artificial records based on a dataset of real records in this research. PSO algorithm is combined with the model for finding the best values of learning parameters. In addition, wavelet packet transform is used for decomposing the earthquake signal to use as the suitable output of network. Despite of original brain emotional model, the proposed modified parallel model is applicable on multiinput–output data. Numerical examples show that the proposed model in this research could be successfully used for generating artificial records with acceptable error for statistical properties of the required pseudo spectral acceleration.

2016

Brain Emotional Learning-Based Prediction Model For Long-Term Chaotic Prediction Applications (2016) [link]
This study suggests a new prediction model for chaotic time series inspired by the brain emotional learning of mammals. We describe the structure and function of this model, which is referred to as BELPM (Brain Emotional Learning- Based Prediction Model). Structurally, the model mimics the connection between the regions of the limbic system, and functionally it uses weighted k nearest neighbors to imitate the roles of those regions. The learning algorithm of BELPM is defined using steepest descent (SD) and the least square estimator (LSE). Two benchmarks chaotic time series, Lorenz and Henon, have been used to evaluate the performance of BELPM. The obtained results have been compared with those of other prediction methods. The results show that BELPM has the capability to achieve a reasonable accuracy for long-term prediction of chaotic time series, using a limited amount of training data and a reasonably low computational time.

BELBIC Tuned PI Controller Based Chopper Driven PMDC Motor (2016) [link]
In orthopaedic surgeries, permanent magnet DC motors are used to drill the bone and fix the screws. The Motor drive employs an inner current and outer speed control loop with a conventional or modern controller. To enhance the performance of the drive, this paper proposes a Brain Emotional Logic Based Intelligent Controller based chopper drive. The proposed drive scheme has been simulated using Matlab/Simulink and physically realized for validation. A comparative analysis has been made between the conventional PI controller based drive and the proposed system in order to prove that the proposed scheme has an edge over the traditional PI controller scheme counterpart.

Improving maximum power point tracking of partially shaded photovoltaic system by using IPSO-BELBIC (2016) [link]
Solar photovoltaic (PV) arrays in remote applications are often related to the rapid changes in the partial shading pattern. Rapid changes of the partial shading pattern make the tracking of maximum power point (MPP) of the global peak through the local ones too difficult. An essential need to make a fast and efficient algorithm to detect the peaks values which always vary as the sun irradiance changes. This paper presents two algorithms based on the improved particle swarm optimization technique one of them with PID controller (IPSO-PID), and the other one with Brain Emotional Learning Based Intelligent Controller (IPSO-BELBIC). These techniques improve the maximum power point (MPP) tracking capabilities for photovoltaic (PV) system under partial shading circumstances. The main aim of these improved algorithms is to accelerate the velocity of IPSO to reach to (MPP) and increase its efficiency. These algorithms also improve the tracking time under complex irradiance conditions. Based on these conditions, the tracking time of these presented techniques improves to 2 msec, with an efficiency of 100%.

A New Hybrid BFOA-PSO Optimization Technique for Decoupling and Robust Control of Two-Coupled Distillation Column Process (2016) [link]
The two-coupled distillation column process is a physically complicated system in many aspects. Specifically, the nested interrelationship between system inputs and outputs constitutes one of the significant challenges in system control design. Mostly, such a process is to be decoupled into several input/output pairings (loops), so that a single controller can be assigned for each loop. In the frame of this research, the Brain Emotional Learning Based Intelligent Controller (BELBIC) forms the control structure for each decoupled loop. The paper’s main objective is to develop a parameterization technique for decoupling and control schemes, which ensures robust control behavior. In this regard, the novel optimization technique Bacterial Swarm Optimization (BSO) is utilized for the minimization of summation of the integral time-weighted squared errors (ITSEs) for all control loops. This optimization technique constitutes a hybrid between two techniques, which are the Particle Swarm and Bacterial Foraging algorithms. According to the simulation results, this hybridized technique ensures low mathematical burdens and high decoupling and control accuracy. Moreover, the behavior analysis of the proposed BELBIC shows a remarkable improvement in the time domain behavior and robustness over the conventional PID controller.

A winner-take-all approach to emotional neural networks with universal approximation property (2016) [link]
In this article, a brain-inspired winner-take-all emotional neural network (WTAENN) architecture is proposed and then the universal approximation property for this kind of architecture is proved. WTAENN is a single layered feedforward neural network that benefits from the excitatory, inhibitory, and expandatory neural connections as well as the winner-take-all (WTA) competitions in the human brain’s nervous system. The universal approximation capability of the proposed architecture is illustrated on two example functions and then applied to several competing benchmark problems such as curve fitting, pattern recognition, classification and prediction. In particular, it is tested on twelve UCI classification datasets, a facial recognition problem, three real world prediction problems (2 chaotic time series of geomagnetic activity indices and wind farm power generation data), two synthetic case studies with constant and nonconstant noise variance as well as k-selector and linear programming problems. The results indicate the general applicability and often superiority of the approach in terms of higher accuracy and lower model complexity, especially where low computational complexity is imperative.

BELBIC-Sliding Mode Control of Robotic Manipulators With Uncertainties and Switching Constraints (2016) [link]
This paper addresses the control problem for trajectory tracking of a class of robotic manipulators presenting uncertainties and switching constraints using a biomimetic approach. Uncertainties, system-inherent as well as environmental disturbances deteriorate the performance of the system. A change in constraints between the robot’s end-effector and the environment resulting in a switched nonlinear system, undermines the stable system performance. In this work, a robust adaptive controller combining sliding mode control and BELBIC (Brain Emotional Learning-Based Intelligent Control) is suggested to remediate the expected impacts on the overall system tracking performance and stability. The controller is based on an interplay of inputs relating to environmental information through error-signals of position and sliding surfaces and of emotional signals regulating the learning rate and adapting the future behaviour based on prior experiences. The proposed control algorithm is designed to be applicable to discontinuous freeform geometries. Its stability is proven theoretically and a simulation, performed on a two-link manipulator verifies its efficacy.

2015

Human Inspired Control System for an Unmanned Ground Vehicle (2015) [link]
In this research work, a novel control system strategy for the robust control of an
unmanned ground vehicle is proposed. This strategy is motivated by efforts to mitigate the problem for scenarios in which the human operator is unable to properly communicate with the vehicle. This novel control system strategy consisted of three major components: I.) Two independent intelligent controllers, II.) An intelligent navigation system, and III.) An intelligent controller tuning unit. The inner workings of the first two components are based off the Brain Emotional Learning (BEL), which is a mathematical model of the Amygdala-Orbitofrontal, a region in mammalians brain known to be responsible for emotional learning. Simulation results demonstrated the implementation of the BEL model to be very robust, efficient, and adaptable to dynamical changes in its application as controller and as a sensor fusion filter for an unmanned ground vehicle. These results were obtained with significantly less computational cost when compared to traditional methods for control and sensor fusion. For the intelligent controller tuning unit, the implementation of a human emotion recognition system was investigated. This system was utilized for the classification of driving behavior. Results from experiments showed that the affective states of the driver are accurately captured. However, the driver’s affective state is not a good indicator of the driver’s driving behavior. As a result, an alternative method for classifying driving behavior from the driver’s brain activity was explored. This method proved to be successful at classifying the driver’s behavior. It obtained results comparable to the common approach through vehicle parameters. This alternative approach has the advantage of directly classifying driving behavior from the driver, which is of particular use in UGV domain because the operator’s information is readily available. The classified driving mode was used tune the controllers’ performance to a desired mode of operation. Such qualities are required for a contingency control system that would allow the vehicle to operate with no operator inputs.

Dynamic Classification Based Brain Emotional Learning for EEG Signal Processing in P300-based Brain and Computer Interface (2015) [link]
Today, the interest in brain and computer interfaces has rapidly grown owing to the possibility of providing disabled subjects with new communication channels. Despite these interests, there are some obstacles in providing applicable BCIs. One of these obstacles is the non-stationary nature of brain signals varying from trial-to-trial and subject-to-subject. To overcome this problem, we need to design dynamic systems to adapt them to this data.

2014

A simple Mathematical Fuzzy Model of Brain Emotional Learning to Predict Kp Geomagnetic Index (2014) [link]
In this paper, we propose fuzzy mathematical model of brain limbic system (LS) which is responsible for emotional stimuli. Here the proposed model is utilized to predict the chaotic activity of the earth’s magnetosphere. Numerical results show that the correlation of the results obtained from the proposed fuzzy model is higher than non-fuzzy models. Hence, the proposed model can be applied in real time chaotic time series prediction.

A Brain Emotional Learning-based Prediction Model for the Prediction of Geometric Storms (2014) [link]
This paper introduces a new type of brain emotional learning inspired models (BELIMs). The suggested model is utilized as a suitable model for predicting geomagnetic storms. The model is known as BELPM which is an acronym for Brain Emotional Learning-based Prediction Model. The structure of the suggested model consists of four main parts and mimics the corresponding regions of the neural structure underlying fear conditioning. The functions of these parts are implemented by assigning adaptive networks to the different parts. The learning algorithm of BELPM is based on the steepest descent (SD) and the least square estimator (LSE). In this paper, BELPM is employed to predict geomagnetic storms using the Disturbance Storm Time (Dst) index. To evaluate the performance of BELPM, the obtained results have been compared with the results of the adaptive neuro-fuzzy inference system (ANFIS).

A self-tuning brain emotional learning–based intelligent controller for trajectory tracking of electrohydraulic actuator (2014) [link]
Realization of biologically motivated algorithms in industrial applications is becoming a new research, especially in the field of electrohydraulic systems. One of the recent innovations named brain emotional learning–based intelligent controller has been catching eyes of the researcher as a model-free adaptive controller, which has effective capabilities to handle nonlinearities and uncertainties of controlled systems. The aim of this article is to develop a so-called self-tuning brain emotional learning–based intelligent controller for tracking control of electrohydraulic actuators. Here, the main control unit brain emotional learning–based intelligent controller is used to drive the system to desired targets. Meanwhile, a fuzzy inference is designed to tune online the reward function (RF) parameter of the brain emotional learning–based intelligent controller, which enables the system robustness and stability. A test rig employing an electrohydraulic actuator is then setup to investigate the system control performance. The experimental results implied that proposed controller has strong ability to drive the system to follow different reference trajectories with minimal errors.

EMOTIONAL LEARNING BASED INTELLIGENT CONTROLLERS FOR ROTOR FLUX ORIENTED
CONTROL OF INDUCTION MOTOR (2014) [link]

This paper presents design and evaluation of a novel approach based on emotional learning to improve the speed control system of rotor flux oriented control of induction motor. The controller includes a neuro-fuzzy system with speed error and its derivative as inputs. A fuzzy critic evaluates the present situation, and provides the emotional signal (stress). The controller modifies its characteristics so that the critics stress is reduced. The comparative simulation results show that the proposed controller is more robust and hence found to be a suitable replacement of the conventional PI controller for the high performance industrial drive applications.

Brain Emotional Learning-Inspired Models (2014) [link]
In this thesis the mammalian nervous system and mammalian brain have been used as inspiration to develop a computational intelligence model based on the neural structure of fear conditioning and to extend the structure of the previous proposed amygdala-orbitofrontal model. The proposed model can be seen as a framework for developing general computational intelligence based on the emotional system instead of traditional models on the rational system of the human brain. The suggested model can be considered a new data driven model and is referred to as the brain emotional learning-inspired model (BELIM). Structurally, a BELIM consists of four main parts to mimic those parts of the brain’s emotional system that are responsible for activating the fear response. In this thesis the model is initially investigated for prediction and classification. The performance has been evaluated using various benchmark data sets from prediction applications, e.g. sunspot numbers from solar activity prediction, auroral electroject (AE) index from geomagnetic storms prediction and Henon map, Lorenz time series. In most of these cases, the model was tested for both long-term and short-term prediction. The performance of BELIM has also been evaluated for classification, by classifying binary and multiclass benchmark data sets.

Brain Emotional Learning Based Intelligent Controller via Temporal Difference Learning (2014) [link]
Modeling emotions has attracted much attention in recent years, both in cognitive psychology and design of artificial systems. Far from being a negative factor in decision-making, emotions have shown to be a strong faculty for making fast satisfying decisions. In this paper, we have adapted a computational model based on the limbic system in the mammalian brain for control engineering applications. Learning in this model based on Temporal Difference (TD) Learning. We applied the proposed controller (termed BELBIC) for a simple model of a submarine. The model was supposed to reach the desired depth underwater. Our results demonstrate excellent control action, disturbance handling and system parameter robustness for TDBELBIC. The proposed method, regarding the present conditions, the system action in the part and the controlling aims, can control the system in a way that these objectives are attained in the least amount of time and the best way.

A Neural Basis Computational Model of Emotional Brain for Online Visual Object Recognition (2014) [link]
In this study, we propose a novel visual object recognizer inspired by the human brain’s emotional learning. In the proposed computational model, the visual information is transferred through the ventral visual pathway to the amygdala, which is responsible for emotional visual stimuli. In the model, the orbitofrontal cortex (OFC) evaluates the amygdala response and tries to prevent inappropriate answers. The proposed visual recognizer is based on threshold logic units defined on the neural models of the amygdala and the OFC. According to the experimental results, the presented model learns the visual patterns quickly and shows higher performance than the brain emotional learning-based pattern recognizer (BRLPR) and multilayer perceptron (MLP) with Levenberg–Marquardt backpropagation (BPG) learning algorithm, in which the adaptive neurofuzzy inference system (ANFIS) cannot be trained because of the curse of dimensionality. The main features of the proposed model are the lower time and spatial complexity. Hence, it can be utilized in real-time visual object recognition.

Design of Brain Emotional Learning Based Intelligent Controller (BELBIC) for uncertain systems (2014) [link]
This paper addressess, a study on the bio inspired intelligent controller namely Brain Emotional Learning Based Intelligent Controller (BELBIC) and its design for spring mass damper system which has parameter uncertainty. The mass-damper-spring system is a common control experimental device frequently used. This paper gives the possible structured (parametric) uncertainties in the spring-mass-damper system. The model free controller based on the brain-emotional-learning algorithm adapts the computational model of limbic system in the mammalian brain. Here an attempt is made to design the controller for a uncertain system (spring-mass-damper). The performance of the developed controller is compared with that of a PID controller.

Automatic speed control of an asymmetrical six-phase induction motor using emotional controller (BELBIC) (2014) [link]
An induction motor (IM) with two sets of three-phase windings in stator is called a six-phase IM (SPIM). SPIM has lower torque pulsation compared to a three-phase IM and using SPIM, the power rating of inverter legs can be reduced. This paper offers a high performance intelligent controller for speed automatic control of a SPIM. A brain emotional learning based intelligent controller (BELBIC) is proposed as the speed controller. This emotional controller has simple structure with high auto learning feature and it is independent of parameters variations. A direct vector control in rotor flux frame based on space vector pulse width modulation (SVPWM) is used to decouple flux and torque control of the SPIM like a conventional three-phase machine. In this work, the d-axis current and speed reference signals are accurately tracked. An overview of the BELBIC is provided. A comparison with the conventional PI controller and Sliding Mode Control (SLMC) points out the superiority of the proposed BELBIC in speed response and torque ripple. Experimental results indicate that the BELBIC achieves a good speed regulation with a less control effort and torque ripple.

Brain Emotional Learning Control System Design for Nonlinear Systems (2014) [link]
Brain emotional learning controller is constructed based on the physical meaning of human’s brain; this controller uses neural network to imitate the judgment and emotion factors of brain. This paper proposes a brain emotional learning intelligent algorithm; beside the learning algorithm like neural network, it also included the calculation algorithm of emotion factor. Beside self-adjust the weight trough learning, this algorithm can self-judge the emotion factor and includes it into the calculation algorithm so as to achieve more intelligent algorithm. An example, the three-tank system, is demonstrated to illustrate the effectiveness of the proposed control method. Simulation results show that the proposed controller can achieve satisfactory control performance for the liquid level control of the three tank system.

2013

Chaotic Time Series Prediction Using Brain Emotional Learning Based Recurrent Fuzzy System (BELRFS) (2013) [link]
In this paper, an architecture based on the anatomical structure of the emotional network in the brain of mammalians is applied as a prediction model for chaotic time series studies. The architecture is called brain emotional learning–based recurrent fuzzy system (BELRFS), which stands for: brain emotional learning–based recurrent fuzzy system. It adopts neuro–fuzzy adaptive networks to mimic the functionality of brain emotional learning. In particular, the model is investigated to predict space storms, since the phenomenon has been recognised as a threat to critical infrastructure in modern society. To evaluate the performance of BELRFS, three benchmark time series: Lorenz time series, sunspot number time series and auroral electrojet (AE) index. The obtained results of BELRFS are compared with linear neuro–fuzzy (LNF) with the locally linear model tree algorithm (LoLiMoT). The results indicate that the suggested model outperforms most of data driven models in terms of prediction accuracy.

A simple Mathematical Fuzzy Model of Brain Emotional Learning to Predict Kp Geomagnetic Index (2013) [link]
n this paper, we propose fuzzy mathematical model of brain limbic system (LS) which is responsible for emotional stimuli. Here the proposed model is utilized to predict the chaotic activity of the earth’s magnetosphere. Numerical results show that the correlation of the results obtained from the proposed fuzzy model is higher than non-fuzzy models. Hence, the proposed model can be applied in real time chaotic time series prediction.

Mathematical modeling of emotional brain for classification problems (2013) [link]
Recently various models of mammalian’s brain emotional learning (BEL) have been successfully utilized in specific control applications and prediction problems. In this paper, a BEL based classifier (BELC) is presented. The distinctive feature of BELC is applying the activation function tansig in the model. In the numerical studies, various comparisons are made between BELC and multilayer perceptron (MLP) to classify 6 UCI datasets. According to the numerical studies, BELC shows higher accuracy and lower computational complexity in single class classification and can be utilized in real time classification problems.

Adaptive brain emotional decayed learning for online prediction of geomagnetic activity indices (2013) [link]
In this paper we propose adaptive brain-inspired emotional decayed learning to predict Kp, AE and Dst indices that characterize the chaotic activity of the earth’s magnetosphere by their extreme lows and highs. In mammalian brain, the limbic system processes emotional stimulus and consists of two main components: Amygdala and Orbitofrontal Cortex (OFC). Here, we propose a learning algorithm for the neural basis computational model of Amygdala–OFC in a supervised manner and consider a decay rate in Amygdala learning rule. This added decay rate has in fact a neurobiological basis and yields to better learning and adaptive decision making as illustrated here. In the experimental studies, various comparisons are made between the proposed method named ADBEL, Multilayer Perceptron (MLP), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Locally Linear Neuro-Fuzzy (LLNF). The main features of the presented predictor are the higher accuracy at all points especially at critical points, lower computational complexity and adaptive training. Hence, the presented model can be utilized in adaptive online prediction problems.

Brain emotional learning-based pattern recognizer (2013) [link]
In this article, the brain emotional learning-based pattern recognizer (BELPR) is proposed to solve multiple input–multiple output classification and chaotic time series prediction problems. BELPR is based on an extended computational model of the human brain limbic system that consists of an emotional stimuli processor. The BELPR is model free and learns the patterns in a supervised manner and evaluates the output(s) using the activation function tansig. In the numerical studies, various comparisons are made between BELPR and a multilayer perceptron (MLP) with a back-propagation learning algorithm. The methods are tested to classify 12 UCI (University of California, Irvine) machine learning data sets and to predict activity indices of the Earth’s magnetosphere. The main features of BELPR are higher accuracy, decreased time and spatial complexity, and faster training.

2012

Neuro-fuzzy models, BELRFS and LoLiMoT, for prediction of chaotic time series (2012) [link]
This paper suggests a novel learning model for prediction of chaotic time series, brain emotional learning-based recurrent fuzzy system (BELRFS). The prediction model is inspired by the emotional learning system of the mammal brain. BELRFS is applied for predicting Lorenz and Ikeda time series and the results are compared with the results from a prediction model based on local linear neuro-fuzzy models with linear model tree algorithm (LoLiMoT).

Brain Emotional Learning Based Fuzzy Inference System (BELFIS) for Solar Activity Forecasting (2012) [link]
This paper presents a new architecture based on a brain emotional learning model that can be used in a wide varieties of AI applications such as prediction, identification and classification. The architecture is referred to as: Brain Emotional Learning Based Fuzzy Inference System (BELFIS) and it is developed from merging the idea of prior emotional models with fuzzy inference systems. The main aim of this model is presenting a desirable learning model for chaotic system prediction imitating the brain emotional network. In this research work, the model is used for predicting the solar activity, since it has been recognized as a threat to critical infrastructures in modern society. Specifically sunspot numbers are predicted by applying the proposed brain emotional learning model. The prediction results are compared with the outcomes of using other previous models like the locally linear model tree (LOLIMOT) and radial bias function (RBF) and adaptive neuro-fuzzy inference system (ANFIS).

Application of emotional learning fuzzy inference systems and locally linear neuro-fuzzy models for prediction and simulation in dynamic systems (2012) [link]
Mathematical description and modeling of dynamic systems is challenging due to their high level of complexity, their nonlinear and chaotic behaviors, the presence of uncertainties and interference of human behavior in their outputs, and their time-variant nature. Because of such characteristics and the importance of dynamic systems modeling, high-performance modeling tools are required to analyze, identify, model and finally control such systems. Emotional learning fuzzy inference system (ELFIS) and locally linear neuro-fuzzy (LLNF) model can be considered as two potential tools for modeling and prediction of dynamic systems. In this paper ELFIS and LLNF are applied to three various dynamic systems, namely electricity price forecasting in competitive power markets, stock market prediction and prediction of surface ozone concentration. the comparisons between the applied methods (LLNF and ELFIS) and some other methods such as multi-layer perceptron (MLP) neural networks, demonstrated the superiority and computational efficiency of the proposed approaches over the other methods, besides their greater comprehensibility and transparency for dynamic systems modeling and prediction.

Supervised brain emotional learning (2012) [link]
In this paper we propose the supervised version of neuro-based computational model of brain emotional learning (BEL). In mammalian brain, the limbic system processes emotional stimulus and consists of following two main components: amygdala and orbitofrontal cortex (OFC). Recently several models of BEL based on monotonic reinforcement learning in amygdala are proposed by researchers. Here, we introduce supervised version of BEL which can be learned by pattern-target examples. According to the experimental studies, where various comparisons are made between the proposed method, multilayer perceptron (MLP) and adaptive neuro-fuzzy inference system (ANFIS), the main feature of the presented method is fast training in prediction problems.

Brain emotional learning based intelligent controller for stepper motor trajectory tracking (2012) [link]
Excellent attributes of permanent magnet stepper motor (PMSM) make it prominent in robotic, aerospace, and numerical machine applications. However, the problem of nonlinearity and presence of mechanical configuration changes, particularly in precision reference trajectory tracking, must be put into perspective. In this paper, a novel cognitive strategy based on the emotional learning in limbic system of mammalian’s brain is employed to establish an intelligent controller in order to provide the necessary control actions as to achieve trajectory tracking of the rotor speed in different circumstances. Brain emotional learning based intelligent controller (BELBIC) is a model free controller, independent of model dynamic and variations that occurs in system, can be taken in to account as an outstanding option for the nonlinear applications. Fast response, high accuracy, and the ability of disturbance rejection introduce BELBIC as an eminent controller. To verify these attributes, different test beds have been simulated in Matlab Simulink environment and the performance of BELBIC is investigated. For further illumination, a classic controller called static proportional-integral-derivative (PID) is also applied on the model and then a comprehensive comparison, both in certain and uncertain condition, between the results of the proposed controllers is done. Uncertain situation is provided by applying load torque disturbance and variation in parameters of PMSM. The results of simulations clearly indicate the outstanding ability of BELBIC in speed tracking with high accuracy for the arbitrary reference signals and conspicuous robustness of this controller in presence of uncertainties.

Position Control of Hybrid Stepper Motor Using Brain Emotional Controller (2012) [link]
In order to control the position of hybrid stepper motor and improve its performance, direct torque control strategy is adopted. The main idea of this paper is to present the implementation of an emotional controller for position control of hybrid stepper motor drive. The proposed controller is called Brain Emotional Learning Based Intelligent Controller (BELBIC). This controller is a computational model of emotional processing mechanism in the brain. The effectiveness of the proposed BELBIC controller-based hybrid stepper motor drive is verified by simulation results.

Applying Brain Emotional Learning Based Intelligent Controller (Belbic) to Multiple‐Area Power Systems (2012) [link]
Evolution of efficient power system control is very important. An effective power system simulation is useful for development as an evaluation of control performance. In this paper, a new, efficient simulation of multiple‐area power system control is proposed. We present the application of a Brain Emotional Learning Based Intelligent Controller (BELBIC) to regulate the frequency error for a two‐area interconnected power system. BELBIC is based on the emotional learning process in the Amygdala‐Orbitofrontal system of the mammalian brain. Simulation results of this controller and the PID controller for a two‐area power system in a matlab/simulink environment show that it develops the stability control performance and improves amplitude of oscillations and settling time up to 17% and 24%, respectively. Actually, the simulation shows that the proposed BELBIC model for the matlab/simulink environment works and gives acceptable results, without redesigning it for each separate system.

2011

Emotional controller (BELBIC) based DTC for encoderless Synchronous Reluctance Motor drives (2011) [link]
In this paper, a direct torque control (DTC) of encoderless Synchronous Reluctance Motor (SynRM) drives is proposed based on emotional controller and space vector modulation. The proposed modern controller is called brain emotional learning based intelligent controller (BELBIC). The utilization of BELBIC is based on the emotion processing mechanism in brain, and is essentially an action, which is based on sensory inputs and emotional cues. This intelligent control is inspired by the limbic system of mammalian brain. In this work, a BELBIC controller is designed for torque and flux control in stator flux reference frame, respectively. The proposed controller is able to reduce the torque, flux, current and speed pulsations during steady-state behavior while the fast response and robustness merits of the classic DTC are preserved. In addition, in order to achieve a maximum torque per Ampere (MTPA) strategy at any operating condition, a search algorithm changes the stator flux magnitude. The proposed controller is successfully implemented in real-time through a PC-based three-phase, 0.5 Hp SynRM. The obtained results show superior proposed control characteristics, especially very fast response, simple implementation and robustness with respect to disturbances and parameter variations. So the proposed encoderless MTPA emotional controller for SynRM drives with minimized number of dependent parameters presents excellent promise for industrial scale utilization.

2010

Emotional controller (BELBIC) for electric drives — A review (2010) [link]
Artificial intelligence (AI) and Biologically-inspired techniques, particularly the neural networks, are recently having significant impact on power electronics and electric drives. Neural networks have created a new and advancing frontier in power electronics, which is already a complex and multidisciplinary technology that is going through dynamic evolution in the recent years. But recently, a new type of the intelligent techniques, for control and decision making processes, was introduced that is based on the emotion processing mechanism in brain, and is essentially an action selection, which is based on sensory inputs and emotional cues. This intelligent control is inspired by the limbic system of mammalian brain. The proposed controller is called brain emotional learning based intelligent controller (BELBIC). This paper gives a comprehensive introduction and perspective of its applications in the intelligent control for electric drives area. The principal topologies of neural networks that are currently most relevant for applications in power electronics have been reviewed including the detailed description of their properties. Both feedforward and feedback or recurrent architectures have been covered in the description. The application examples that are discussed in this paper include different electric drives control as: Direct Current Motors (DC), Alternative Current Motors (AC) and Special Motors (SRM). In addition, almost all of the selected applications in the literature are included in the references. In the experimental and simulation works, novel and simple implementations of the drives system were achieved by using the intelligent controller, which control the motor speed accurately in different operating points. This emotional intelligent controller has simple structure with high auto learning feature that does not require any motor parameters, for self performance. The proposed emotional controller has been experimentally implemented in some of the laboratory electric drives, and shows excellent promise for industrial scale utilization.

PSO-BELBIC scheme for two-coupled distillation column process (2010) [link]
In the two-coupled distillation column process, keeping the tray temperatures within a specified range around their steady state values assures the specifications for top and bottom prod- uct purity. The two-coupled distillation column is a 4 Input/4 Output process. Normally, control engineers decouple the process into four independent loops. They assign a PID controller to control each loop. Tuning of conventional PID controllers is very difficult when the process is subject to external unknown factors. The paper proposes a Brain Emotional Learning Based Intelligent Con- troller (BELBIC) to replace conventional PID controllers. Moreover, the values of BELBIC and PID gains are optimized using a particle swarm optimization (PSO) technique with minimization of Integral Square Error (ISE) for all loops. The paper compares the performance of the proposed PSO-BELBICs with that of conventional PSO-PID controllers. PSO-BELBICs prove their useful- ness in improving time domain behavior with keeping robustness for all loops.

2009

Emotion on FPGA (2009) [link]
Implementation of intelligent and bio-inspired algorithms in industrial and real applications is arduous, time consuming and costly; in addition, many aspects of system from high level behavior of algorithm to energy consumption of targeted system must be considered simultaneously in the design process. Advancement of hardware platforms such as DSPs, FPGAs and ASICs in recent years has made it increasingly possible to implement computationally complex intelligent systems; on the other hand, however, the design and testing costs of these systems are high. Reusability and extendibility features of the developed models can decrease the total cost and time-to-market of an intelligent system. In this work, model driven development approach is utilized for implementation of emotional learning as a bio-inspired algorithm for embedded purposes. Recent studies show that emotion is a mechanism for fast decision making in human and other animals, and can be assumed as an expert system. Mathematical models have been developed for describing emotion in mammals from cognitive studies. Here brain emotional based learning intelligent controller (BELBIC), which is based on mammalian middle brain, is designed and implemented on FPGA and the obtained embedded emotional controller (E-BELBIC) is utilized for controlling real laboratorial overhead traveling crane in model-free and embedded manner. Short time-to-market, easy testing and error handling, separating concerns, improving reusability and extendibility of obtained models in similar applications are some benefits of the model driven development methodology.

2008

Learning based brain emotional intelligence as a new aspect for development of an alarm system (2008) [link]
The multi criteria and purposeful prediction approach has been introduced and is implemented by the fast and efficient behavioral based brain emotional learning method. On the other side, the emotional learning from brain model has shown good performance and is characterized by high generalization property. New approach is developed to deal with low computational and memory resources and can be used with the largest available data sets. The scope of paper is to reveal the advantages of emotional learning interpretations of brain as a purposeful forecasting system designed to warning; and to make a fair comparison between the successful neural (MLP) and neurofuzzy (ANFIS) approaches in their best structures and according to prediction accuracy, generalization, and computational complexity. The auroral electrojet (AE) index are used as practical examples of chaotic time series and introduced method used to make predictions and warning of geomagnetic disturbances and geomagnetic storms based on AE index.

Reinforcement _recurrent fuzzy rule based system based on brain emotional learning structure to predict the complexity dynamic system (2008) [link]
In this study, new approach based on brain emotional Learning process is presented to predict chaotic system more accurate than other learning models. So the main scope of this paper is to reveal the advantages of this learning model that imitate the internal representation of brain emotional learning model to provide a correct response to stimuli to state a purposeful predicting system. The convergence theory is clarified by utilizing the model to predict the complex dynamical Lorenz system. Also the consequence of using this method to forecast such a complex system is compared with obtained results from other related studies that examine other methods such as Locally Linear Model Tree(LOLIMOT) and radial basis function (RBF) Neural network with Orthogonal lest square (OLS ) for predicting the Lorenz chaotic time series. The comparison indicates the superior performance of presented method to make the multi step ahead prediction. Also the effect of noise on the performance of the techniques is also considered. In deed, the learning methods could deal with predicting the future state of complex system with limited training data as well as large data set.

Prediction the price of Virtual Supply Chain Management with using emotional methods (2008) [link]
This study suggests a new prediction model for chaotic time series inspired by the brain emotional learning of mammals. We describe the structure and function of this model, which is referred to as BELPM (Brain Emotional Learning-Based Prediction Model). Structurally, the model mimics the connection between the regions of the limbic system, and functionally it uses weighted k nearest neighbors to imitate the roles of those regions. The learning algorithm of BELPM is defined using steepest descent (SD) and the least square estimator (LSE). Two benchmark chaotic time series, Lorenz and Henon, have been used to evaluate the performance of BELPM. The obtained results have been compared with those of other prediction methods. The results show that BELPM has the capability to achieve a reasonable accuracy for long-term prediction of chaotic time series, using a limited amount of training data and a reasonably low computational time.

2007

Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition (2007) [link]
This paper proposes an algorithm for optimization inspired by the imperialistic competition. Like other evolutionary ones, the proposed algorithm starts with an initial population. Population individuals called country are in two types: colonies and imperialists that all together form some empires. Imperialistic competition among these empires forms the basis of the proposed evolutionary algorithm. During this competition, weak empires collapse and powerful ones take possession of their colonies. Imperialistic competition hopefully converges to a state in which there exist only one empire and its colonies are in the same position and have the same cost as the imperialist. Applying the proposed algorithm to some of benchmark cost functions, shows its ability in dealing with different types of optimization problems.

Brain emotional learning based intelligent controller applied to neurofuzzy model of micro-heat exchanger (2007) [link]
In this paper, an intelligent controller is applied to govern the dynamics of electrically heated micro-heat exchanger plant. First, the dynamics of the micro-heat exchanger, which acts as a nonlinear plant, is identified using a neurofuzzy network. To build the neurofuzzy model, a locally linear learning algorithm, namely, locally linear mode tree (LoLiMoT) is used. Then, an intelligent controller based on brain emotional learning algorithm is applied to the identified model. The intelligent controller is based on a computational model of limbic system in the mammalian brain. The brain emotional learning based intelligent controller (BELBIC) based on PID control is adopted for the micro-heat exchanger plant. The contribution of BELBIC in improving the control system performance is shown by comparison with results obtained from classic PID controller without BELBIC. The results demonstrate excellent improvements of con- trol action, without any considerable increase in control effort for PID + BELBIC.

2006

INTELLIGENT MODELING AND CONTROL OF WASHING MACHINE USING LOCALLY LINEAR NEURO‐FUZZY (LLNF) MODELING AND MODIFIED BRAIN EMOTIONAL LEARNING BASED INTELLIGENT CONTROLLER (BELBIC) (2006) [link]
Intelligent control of home appliances has, in recent years, attracted much theoretical attention, as well as becoming a major factor for industrial and economic success and rapid market penetration. Washing Machines represent an important market. Intelligent control techniques are capable of providing useful means for both easier use and energy and water conservation. In this paper, the authors use two techniques that have successfully been used in other intelligent modeling and control applications. Firstly, the authors use a neuro‐fuzzy locally linear model tree system for data driven modeling of the machine. Secondly, the authors use a neural computing technique, based on a mathematical model of amygdala and the limbic system, for emotional control of the washing machine. The obtained results indicate the applicability of the proposed techniques in this important business sector.

Applying brain emotional learning algorithm for multivariable control of HVAC systems (2006) [link]
In this paper, we apply a modified version of Brain Emotional Learning (BEL) controller for Heating, Ventilating and Air Conditioning (HVAC) control system whose multivariable, nonlinear and non-minimum phase nature makes the task difficult. The proposed biologically-motivated algorithm achieves robust and satisfactory performance even though there are more than one control inputs to the plant, which may be used to get the desired performance. The response time is also very fast despite the fact that the control strategy is based on satisficing decision making. The proposed strategy is very flexible and alternative performance specifications can easily be enforced via defining proper emotional cues. Simulation results reveal the effectiveness of the approach.

2005

Applying Brain Emotional Learning Algorithm for Multivariable Control of HVAC Systems (2005) [link]
In this paper, we apply a modified version of Brain Emotional Learning (BEL) controller for Heating, Ventilating and Air Conditioning (HVAC) control system whose multivariable, nonlinear and non-minimum phase nature makes the task difficult. The proposed biologically-motivated algorithm achieves robust and satisfactory performance even though there are more than one control inputs to the plant, which may be used to get the desired performance. The response time is also very fast despite the fact that the control strategy is based on satisficing decision making. The proposed strategy is very flexible and alternative performance specifications can easily be enforced via defining proper emotional cues. Simulation results reveal the effectiveness of the approach.

Intelligent Modeling and Control of Washing Machines Using LLNF Modeling and Modified BELBIC (2005) [link]
Intelligent control of home appliances, have in recent years, attracted much theoretical attention, as well as becoming a major factor for industrial and economic success and rapid market penetration. Washing machines represent an important market. Intelligent control techniques are capable of providing useful means for both easier use and energy and water conservation. In this paper, we use two techniques that we have successfully used in other intelligent modeling and control applications. Firstly, we use a neuro-fuzzy locally linear model system for data driven modeling of the machine. Secondly, we use a neural computing technique, based on a mathematical model of amygdala and the limbic system, for emotional control of the washing machine. The obtained results indicate the applicability of the proposed techniques in this important business sector.

2004

INTRODUCING BELBIC: BRAIN EMOTIONAL LEARNING BASED INTELLIGENT CONTROLLER (2004) [link]
Modeling emotions has attracted much attention in recent years, both in cognitive psychology and design of artificial systems. Far from being a negative factor in decision making, emotions have shown to be a strong faculty for making fast satisficing decisions. In this paper, we have adapted a computational model based on the limbic system in the mammalian brain for control engineering applications. We applied the proposed controller (termed BELBIC) for some SISO, MIMO and nonlinear systems. Our results demonstrate excellent control action, disturbance handling and system parameter robustness for BELBIC.

Application of Brain Emotional Learning Based Intelligent Controller (BELBIC) to Active Queue Management (2004) [link]
In this paper, Brain Emotional Learning Based Intelligent controller (BELBIC) is applied to Active Queue management (AQM). This type of controller is insensitive to noise and variance of the parameters, thus it is suitable to time varying network systems. Simulation results show the robust performance of BELBIC against the disturbance.

2003

Enhancing the performance of neurofuzzy predictors by emotional learning algorithm (2003) [link]
Neural networks and Neurofuzzy models have been successfully used in the prediction of nonlinear time series. Several learning methods have been introduced to train the Neurofuzzy predictors, such as ANFIS, ASMOD and FUREGA. Many of these methods, constructed over Takagi Sugeno fuzzy inference system, are characterized by high generalization. However, they differ in computational complexity. The emotional Learning, which is successfully used in bounded rational decision making, is introduced as an appropriate method to achieve particular goals in the prediction of real world data. For example, predicting the peaks of sunspot numbers (maximum of solar activity) is more important due to its major effects on earth and satellites. The emotional learning based fuzzy inference system (ELFIS) has the advantages of simplicity and low computational complexity in comparison with other multi-objective optimization methods. The efficiency of proposed predictor is shown in two examples of highly nonlinear time series. Appropriate emotional signal is composed for the prediction of solar activity and price of securities. It is observed that ELFIS performs better predictions in the important regions of solar maximum, and is also a fast and efficient algorithm to enhance the performance of ANFIS predictor in both examples.

Applying BELBIC (Brain Emotional Learning Based Intelligent Controller) to an Autolanding System (2003) [link]
During landing, aircrafts have to face low-altitude wind shear that can be fatal. Most commercial aircrafts currently have optimal automatic landing systems, but they are activated only if well-specified wind speed limitations are met. The reason is that these autolanding systems are not designed to work in the presence of strong wind gusts. In this paper, we apply a modified version of brain emotional learning based intelligent controller (BELBIC) to the autolanding system whose multivariable and non-minimum phase nature make the task difficult. By comparing the results with the results derived from using a high-gain controller, we show that our proposed solution can achieve robust and satisfactory performance.

2000

A computational model of emotional learning in the amygdala (2000) [link]
We describe work in progress with the aim of constructing a computational model of emotional learning and processing inspired by neurophysiological findings. The main brain areas modeled are the amygdala and the orbitofrontal cortex and the interaction between them. We want to show that (1) there exists enough physiological data to suggest the overall architecture of a computational model, (2) emotion plays a clear role in learning the behavior. We review neurophysiological data and present a computational model that is subsequently tested in simulation.