scholarly journals A recurrent wavelet-based brain emotional learning network controller for nonlinear systems

2021 ◽  
Author(s):  
Juncheng Zhang ◽  
Fei Chao ◽  
Hualin Zeng ◽  
Chih-Min Lin ◽  
Longzhi Yang
Keyword(s):  
Nonlinear Systems ◽  
Emotional Learning ◽  
Learning Network ◽  
Network Controller ◽  
Brain Emotional Learning

scholarly journals A Recurrent Wavelet based Brain Emotional Learning Network Controller for Nonlinear Systems

2021 ◽  
Author(s):  
Juncheng Zhang ◽  
Fei Chao ◽  
Hualin Zeng ◽  
Chih-Min Lin ◽  
Longzhi Yang
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Control Systems ◽  
Nonlinear Problems ◽  
Emotional Learning ◽  
Learning System ◽  
Uncertain Environments ◽  
Learning Network ◽  
Brain Emotional Learning ◽  
The Brain

Abstract Conventional control systems often suffer from the co-existence of non-linearity and uncertainty. This paper proposes a novel brain emotional neural network to support addressing such challenges. The proposed network integrates a wavelet neural network into a conventional brain emotional learning network. This is further enhanced by the introduction of a recurrent structure to employ the two networks as the two channels of the brain emotional learning network. The proposed network therefore combines the advantages of the wavelet function, the recurrent mechanism, and the brain emotional learning system, for optimal performance on nonlinear problems under uncertain environments. The proposed network works with a bounding compensator to mimic an ideal controller, and the parameters are updated based on the laws derived from the Lyapunov stability analysis theory. The proposed system was applied to two uncertain nonlinear systems, including a Duffing-Homes chaotic system and a simulated 3-DOF spherical joint robot. The experiments demonstrated that the proposed system outperformed other popular neural-network-based control systems, indicating the superiority of the proposed system.

Online identification of nonlinear systems using neo-fuzzy supported brain emotional learning network

2020 ◽  
Vol 38 (5) ◽  
pp. 6045-6051
Author(s):  
Umar Farooq ◽  
Jason Gu ◽  
Muhammad Usman Asad ◽  
Ghulam Abbas ◽  
Athar Hanif ◽  
...  
Keyword(s):  
Nonlinear Systems ◽  
Emotional Learning ◽  
Learning Network ◽  
Online Identification ◽  
Brain Emotional Learning

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

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Hongmei Li ◽  
Di Li ◽  
Xiangjian Chen ◽  
Zhongqiang Pan
Keyword(s):  
Neural Network ◽  
Lyapunov Function ◽  
Network Model ◽  
Emotional Learning ◽  
Rehabilitation Robot ◽  
Inference System ◽  
Learning Network ◽  
Interval Type ◽  
Brain Emotional Learning

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.

scholarly journals Neo-Fuzzy Integrated Adaptive Decayed Brain Emotional Learning Network for Online Time Series Prediction

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 1037-1049 ◽  
Author(s):  
Houssen S. A. Milad ◽  
Umar Farooq ◽  
Mohamed E. El-Hawary ◽  
Muhammad Usman Asad
Keyword(s):  
Time Series ◽  
Time Series Prediction ◽  
Emotional Learning ◽  
Learning Network ◽  
Brain Emotional Learning

scholarly journals Model-free Adaptive Control Design for Chaotic Systems Via Type-2 Recurrent Wavelet Fuzzy Brain Emotional Learning Networks

2021 ◽  
Author(s):  
Xiaohua Zou ◽  
Qingfeng Yang ◽  
Feng He ◽  
Mingsheng Ling ◽  
Jianping Dai
Keyword(s):  
Adaptive Control ◽  
Chaotic System ◽  
Fuzzy System ◽  
Chaotic Systems ◽  
Sliding Mode ◽  
Emotional Learning ◽  
Learning Network ◽  
Model Free ◽  
Brain Emotional Learning

Abstract This paper introduces an optimal model named Self-Organizing Type-2 Recurrent Wavelet Fuzzy Brain Emotional Learning Network controller (SET2RWFBELNC) with self-evolving algorithm to gain optimal structure from zero initial rule, which merges Interval Type-2 Recurrent Wavelet Fuzzy System and Brain Emotional Learning Network(BELN). As an ideal controller, SET2RWFBELNC not only solves the problem of less information between master and slave systems, but also reduces the influence of external disturbance on synchronization of chaotic systems. Consequently, one model-free adaptive sliding mode controller based on SET2RWFBELNC, sliding model theory, and the asymptotic stability of the synchronization error is realized by robust compensation, in which the strong compensation used for the compensation of the network error. Besides, the Lyapunov function improves the stability of the model. Finally, simulation results of the chaotic system presented in this paper show the superiority of this method. INDEX TERMS Type-2 recurrent wavelet fuzzy system, Sliding mode control, Brain Emotion Learning Network, Model-free adaptive control,Chaotic system.

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