Fuzzy Brain Emotional Learning Research Articles

A 3D Membership Function-Based Type-2 Fuzzy Brain Emotional Learning Predictor for Forecasting Taiwan Stock Price

A 3D Membership Function-Based Type-2 Fuzzy Brain Emotional Learning Predictor for Forecasting Taiwan Stock Price

Fault-Tolerant Control for Robotic Systems Using a Wavelet Type-2 Fuzzy Brain Emotional Learning Controller and a TOPSIS-Based Self-organizing Algorithm

Fault-Tolerant Control for Robotic Systems Using a Wavelet Type-2 Fuzzy Brain Emotional Learning Controller and a TOPSIS-Based Self-organizing Algorithm

Design of Multi-Cell Cooperative Control Algorithm Based on Fuzzy Brain Emotional Learning

Multi-cell cooperative control can be competent for the current increasingly complex biomedical experiments, greatly improving the efficiency of cell manipulation experiments. At present, this kind of multi-cell cooperative control algorithm is becoming more and more important. In this study, holographic optical tweezers are used to capture multiple cells, and a cell manipulation controller is designed based on the Fuzzy Brain Emotional Learning (FBEL) neural network. Firstly, the dynamic model of trapping yeast cells by optical tweezers is analyzed. The distance between the trap position and the cell position is constrained to avoid cell detachment due to the trap moving too fast. Then, the design cell manipulation controller is relied upon to realize single transport trajectory tracking control. Finally, a multi-cell cooperative control algorithm is designed, and combined with the cell manipulation controller, a multi-cell cooperative controller based on the FBEL neural network is formed. The error between the cell position and the desired trajectory is the input of the multi-cell cooperative controller. The output of the multi-cell cooperative controller is the optical trap position, which is used to realize the cooperative control of multiple cells by holographic optical tweezers. The simulation results showed that the multi-cell cooperative controller based on the FBEL neural network can effectively control multiple yeast cells and quickly converge the cell formation, while ensuring a higher control accuracy than other traditional cell manipulation controllers. It provides a new solution for the efficient and precise automatic manipulation of multiple cells.

Encryption and Decryption of Audio Signal and Image Secure Communications Using Chaotic System Synchronization Control by TSK Fuzzy Brain Emotional Learning Controllers.

In this article, a new idea of chaos synchronization and chaos-based secure communication is developed. First, the chaotic master system is used as a transmitter in chaos-based secure communication, then a drive signal is constructed, and the information message is encrypted into the drive signal to form a transmitted signal for secure communication. Second, in the receiver, a recurrent Takagi-Sugeno-Kang (TSK) fuzzy brain emotional learning cerebellar model articulation controller (RTFBECAC) is developed to control the slave system to follow the master system in the transmitter. Third, after descripting the chaotic signal, the embedded information message can be recovered. Besides, the stability problem is analyzed in detail based on the stability theory. Finally, two simulation examples, including audio signal and image, are introduced to illustrate the effectiveness and the advantages of the proposed method.

Wavelet Interval Type-2 Takagi-Kang-Sugeno Hybrid Controller for Time-Series Prediction and Chaotic Synchronization

This paper presents a new hybrid neural network for time series prediction and chaotic synchronization. The proposed controller consists of a wavelet interval type-2 Takagi Sugeno Kang fuzzy brain emotional learning controller (WIT2TFBELC), a wavelet interval type-2 Takagi Sugeno Kang fuzzy cerebellar model articulation controller (WIT2TFCMAC), and a robust compensator (RB). The main controller combines the WIT2TFBELC and the WIT2TFCMAC. A TSK fuzzy system is used to create a hybrid structure with the WIT2TFBELC and the WIT2TFCMAC. The TSK fuzzy system can effectively adjust the weighting for the main controller to achieve the time series prediction and chaotic prediction with better tracking response. Morever a robust compensator is used to achieve robust ability of the system. A Lyapunov function was used to establish the adaptive laws and effectively adjust the system parameters online. Finally, two examples of the application of the proposed algorithm are presented to point out the performance of proposed method.

Emotion Recognition Based on Type-2 Recurrent Wavelet Fuzzy Brain Emotion Learning Network Model

Emotion recognition plays a crucial role in human-robot emotional interaction applications, and the brain emotional learning model is one of several emotion recognition methods, but the learning rules of original brain emotional learning model play poor adaptation and do not work very well. In fact, existing facial emotion recognition methods do not have high accuracy and are not sufficiently practical in real-time applications. In order to solve this problem, this paper introduces an optimal model, which merges interval type-2 recurrent wavelet fuzzy system and brain emotional learning network for emotion recognition. The proposed model takes advantage of type-2 recurrent wavelet fuzzy theory and brain emotional neural network. There are no rules initially, and then the structure and parameters of model are tuning online simultaneously by the gradient approach and Lyapunov function. The system input data streams are directly imported into the neural network through a type-2 recurrent wavelet fuzzy inference system; then, the results are subsequently piped into sensory and emotional channels which jointly produce the final outputs of the network. The proposed model could reduce the uncertainty in terms of vagueness by using type-2 recurrent wavelet fuzzy theory and removing noise samples. Finally, the superior performance of the proposed method is demonstrated by its comparison with some emotion recognition methods on five emotion databases.

Real-Time Adaptive Intelligent Control System for Quadcopter Unmanned Aerial Vehicles With Payload Uncertainties

A novel bidirectional fuzzy brain emotional learning (BFBEL) controller is proposed to control a class of uncertain nonlinear systems such as the quadcopter unmanned aerial vehicle (QUAV). The proposed BFBEL controller is nonmodel-based and has a simplified fuzzy neural network structure and adapts with a novel bidirectional brain emotional learning algorithm. It is applied to control all six degrees-of-freedom of a QUAV for accurate trajectory tracking and to handle the payload uncertainties and disturbances in real-time. The trajectory tracking performance and the ability to handle the payload uncertainties are experimentally demonstrated on a QUAV. The experimental results show a superior performance and rapid adaptation capability of the proposed BFBEL controller. The proposed BFBEL controller can be used for the commercial drone applications.

Data-Driven Control Based on the Interval Type-2 Intuition Fuzzy Brain Emotional Learning Network for the Multiple Degree-of-Freedom Rehabilitation Robot

A novel interval type-2 intuition fuzzy brain emotional learning network model (IT2IFBELC) which depends only on the input and output data is proposed for the rehabilitation robot, which is different from model-based control algorithms that require exact dynamic model knowledge of the rehabilitation robot. The proposed model takes advantage of the type-2 intuition fuzzy theory and brain emotional neural network, and this is no rule initially; then, the structure and parameters of IT2IFBELC are tuned online simultaneously by the gradient approach and Lyapunov function. The system input data streams are directly imported into the neural network through an interval type-2 intuition fuzzy inference system (IT2IFIS), and then the results are subsequently piped into sensory and emotional channels which jointly produce the final outputs of the network. That is, the whole controller is composed of three parts, including the ideal sliding mode controller, the interval type-2 intuition fuzzy brain emotional learning network controller, and a powerful robust compensation controller, and then one Lyapunov function is designed to guarantee the rapid convergence of the control systems. For further illustrating the superiority of this model, several models are studied here for comparison, and the results show that the interval type-2 intuition fuzzy brain emotional learning network model can obtain better satisfactory control performance and be suitable to deal with the influence of the uncertainty of the rehabilitation robot.

Synchronization of Chaotic System Using a Brain-Imitated Neural Network Controller and Its Applications for Secure Communications

This paper proposes a new hybrid algorithm for secure communication applications. The proposed algorithm includes a fuzzy brain emotional learning controller (FBELC), a recurrent cerebellar model articulation controller (RCMAC), and a robust compensator (RC). The main brain-imitated neural network controller is a combination of the RCMAC and the FBELC, which is a mathematical model that approximates the decision and emotional activity of a human brain. A fuzzy inference system is also merged into the FBELC to produce an efficient hybrid structure, then it is used for secure communication applications. The 3-dimensional (3D) Genesio chaotic system is used for audio and image secure communication systems to show the potency and performance of the proposed algorithm. In the first application, a new image encryption algorithm is proposed to enhance security for information transmission, then several standard images are applied for the chaotic synchronization of image secure communication. In the second application, the audio signal is embedded in a 3D chaotic trajectory, which is used as an encryption carrier signal, after using the proposed method for the decryption, the source signal can be retrieved. The comparisons of simulation results using security analyses and root mean square error for recent algorithms are performed to validate the performance and efficiency of the proposed hybrid algorithm. The simulation results point out that our algorithm can attain better synchronization performance, and achieve more efficient audio and image secure communications.

State of health estimation and remaining useful life prediction for lithium-ion batteries using FBELNN and RCMNN

This study proposes the novel method of lithium-ion battery state of health (SoH) estimation and remaining useful life (RUL) prediction to ensure the safety and reliability of the energy storage system. A fuzzy brain emotional learning neural network (FBELNN) is employed to estimate SoH and a recurrent cerebellar model neural network (RCMNN) is used for the RUL prediction. The inputs of FBELNN are extracted features from the monitoring curve of the constant voltage and current, because the lithium-ion battery is seldom completely discharged and the discharging situation in actual operating process is complex. The FBELNN learns the battery aging features that are extracted and selected by discrete wavelet transform and principal component analysis, respectively. The SoH estimation results from the FBELNN are accurate due to the special structure and parameters adaptive laws. The RCMNN and online training again can improve the performance of RUL prediction, because recurrent units can capture the dynamic features. Experimental data are performed by using NASA Prognostics Center of Excellence battery datasets to verify the effectiveness of the proposed method. The results show that the root mean square error of SoH estimation is smaller by the FBELNN and the prediction accuracy of RUL is higher by RCMNN under the different starting points.

Model-free adaptive control for the heading angle of a UAV

The difficulty of accurately modeling the aerodynamics of the heading angle of unmanned aerial vehicles is well known. Furthermore, it is difficult to control the heading angle of an unmanned aerial vehicle (UAV) with a model-based method under the influence of wind disturbances. To address this problem, a novel model-free control strategy is proposed. First, a self-organizing type-2 fuzzy brain emotional learning network model is used to estimate the PPD (pseudo partial derivative). Finally, the stability is guaranteed by designing the Lyapunov function. Meanwhile, the performance of the proposed method is further verified by high-fidelity semi-physical simulations.

H∞ tracking control for nonlinear multivariable systems using wavelet-type TSK fuzzy brain emotional learning with particle swarm optimization

This paper studies the H∞ tracking control for uncertain nonlinear multivariable systems. We propose a control strategy, which combines the adaptive wavelet-type Takagi-Sugeno-Kang (TSK) fuzzy brain emotional learning controller (WTFBELC) and the H∞ robust tracking compensator. As for the adaptive WTFBELC, it is a main controller designed to mimic the ideal controller. The proposed WTFBELC is to obtain much better ability of handling nonlinearities and uncertainties, but the proposed H∞ robust tracking compensator is to compensate the residual error between the adaptive WTFBELC and the ideal controller. Furthermore, the optimal learning rates of the adaptive WTFBELC are searched quickly by using the particle swarm optimization (PSO) algorithm, and the parameter updated laws are derived based on the steepest descent gradient method. The robust tracking performance of this novel control scheme is guaranteed based on Lyapunov stability theory. The mass-spring-damper mechanical system and the three-link robot manipulator, are used to verify the effectiveness of the proposed adaptive PSO-WTFBELC H∞ control scheme.

A hybrid PSO-parallel fuzzy brain emotional learning classifier for medical diseases diagnosis

This study presents a fuzzy brain emotional learning classifier (FBELC), combined with a modified particle swarm optimization (PSO) algorithm, that allows a network to automatically determine the optimum values for a reward signal and a classification threshold. The designed FBELC model imitates the brain decision process including the emotion information. To verify the predictive performance, a novel fitness function based on the accuracy of the training and cross-validation datasets is used for a PSO algorithm. This PSO-FBELC model is used to diagnose breast tumors and heart diseases. A comparison of simulations using the proposed PSO-FBELC with other processes shows that the proposed model performs better in terms of recognition accuracy.

Self-Organizing Adaptive Fuzzy Brain Emotional Learning Control for Nonlinear Systems

This paper aims to develop a more efficient adaptive control system for uncertain nonlinear systems. A novel self-organizing fuzzy brain emotional learning controller (FBELC) is proposed. The FBELC neural network is the mathematical replica of human brain emotions incorporated with fuzzy inference rules, which mimics the judgments and emotions of the brain. The FBELC contains two sub-neural networks, namely a sensory neural network and an emotional neural network. These duet networks influence each other, thus improving the learning ability of the system. The proposed control system also merges sliding mode control to simplify the input space dimension of FBELC. In this study, the self-organization algorithm for the structure of FBELC is developed; thus, the growing and pruning of input layers of FBELC can be automatically proceeded to give the most efficient network structure. Moreover, the parameter adaptive law is derived using gradient descent method to achieve effective learning ability of the network. The proposed control system comprises the self-organizing adaptive FBELC (SOAFC) used as the main controller and a fuzzy compensator designed to obtain robust stability of the system for handling uncertain nonlinear systems. The developed SOAFC is then applied to a double inverted pendulum system and a biped robot to illustrate its effectiveness.

Adaptive filter design for active noise cancellation using recurrent type-2 fuzzy brain emotional learning neural network

This article aims to develop a more efficient adaptive filter for the active noise cancellation (ANC). A novel recurrent interval type-2 fuzzy brain emotional learning filter (RT2BELF) is proposed for achieving favourable filtering performance. The ANC is a method to eliminate noise by creating an anti-noise signal which has the same magnitude but opposite phase with the unwanted noise. In order to adapt to the change of the noise, the parameters for the RIT2BELF are online updated based on the adaptive laws, which are derived by the steepest descent gradient approach. The performance of the proposed ANC design method is successfully demonstrated based on numerical simulation results in the real signals. Finally, the superiority of the proposed method is confirmed by the results comparison with some noise cancellation methods.

A Fuzzy Brain Emotional Learning Classifier Design and Application in Medical Diagnosis

A Fuzzy Brain Emotional Learning Classifier Design and Application in Medical Diagnosis

Wavelet Fuzzy Brain Emotional Learning Control System Design for MIMO Uncertain Nonlinear Systems.

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.

Self-evolving type-2 fuzzy brain emotional learning control design for chaotic systems using PSO

This work presents a design of interval type-2 fuzzy brain emotional learning control (T2FBELC) combining with the self-evolving algorithm to help the network to automatically achieve the optimum construction from the empty initial rule. In the control system design, the T2FBELC is the main controller used to mimic an ideal controller, and a robust controller is a compensator for the compensation of the residual error. Implementing the steepest descent gradient approach, the parameter adaptive laws of the proposed system are derived. Besides, the particle swarm optimization (PSO) is applied to find the optimal learning rates for the parameter adaptive laws. The stability of the proposed algorithm is guaranteed using the Lyapunov function. Finally, the effectiveness of the proposed control system is verified by numerical simulations of the chaotic systems.

A Functional-link-based Fuzzy Brain Emotional Learning Network for Breast Tumor Classification and Chaotic System Synchronization

This paper aims to propose an efficient neural network and applies it for both classification and control problems. A functional link-based fuzzy brain emotional learning network is developed. The proposed network is a generalization of several existing neural networks, such as a function link neural network, a fuzzy neural network and a brain emotional learning network. This network can be used as a classifier in which breast tumors are divided as either benign tumors or malignant tumors. Meanwhile, it can be also applied to control an uncertain nonlinear system. The parameters of the network are all modified online by the derived adaptation laws. In addition, a stable convergence is guaranteed by utilizing the Lyapunov stability theorem. Finally, the proposed method is applied for the breast tumor classification and the chaotic system synchronization. A comparison between the proposed method with other networks show that the proposed network has higher accuracy and sensitivity when serving as a classifier and can reduce the tracking error effectively when serving as a controller.

Fuzzy Brain Emotional Learning Control System Design for Nonlinear Systems

This paper aims to develop a new fuzzy neural network by incorporating a brain emotional learning controller (BELC) with the fuzzy inference rules. The brain has an orbitofrontal cortex and an amygdala. The BELC is a mathematical model, which imitates judgment and emotion of a brain. So that BELC contains two neural networks; the former is a sensory neural network and the latter is an emotional neural network. These two systems affect each other and this will effectively improve the approximation ability of BELC. By incorporating fuzzy inference system into the BELC, a novel fuzzy brain emotional learning controller (FBELC) is proposed which includes two fuzzy inference systems. The parameter updating rules have an interacting term between two systems, which will increase the learning performance of this system. Then, the developed FBELC is applied to control nonlinear systems. Two examples, a chaotic system and an inverted double pendulum system, are demonstrated to illustrate the effectiveness of the proposed control method. A comparison between the proposed FBELC with other controllers shows that the proposed controller can achieve better control performance than the other controllers.