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

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Di Li ◽  
Xiangjian Chen
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Fuzzy Theory ◽  
Learning Model ◽  
Emotional Learning ◽  
Superior Performance ◽  
Learning Network ◽  
Proposed Model ◽  
Brain Emotional Learning

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.

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 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.

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.

scholarly journals Case Study of Deep Learning Model of Temperature-Induced Deflection of a Cable-Stayed Bridge Driven by Data Knowledge

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2293
Author(s):  
Zixiang Yue ◽  
Youliang Ding ◽  
Hanwei Zhao ◽  
Zhiwen Wang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Prior Knowledge ◽  
Learning Model ◽  
Bridge Management ◽  
Learning Network ◽  
Cable Stayed Bridge ◽  
Main Girder ◽  
Deep Learning Model

A cable-stayed bridge is a typical symmetrical structure, and symmetry affects the deformation characteristics of such bridges. The main girder of a cable-stayed bridge will produce obvious deflection under the inducement of temperature. The regression model of temperature-induced deflection is hoped to provide a comparison value for bridge evaluation. Based on the temperature and deflection data obtained by the health monitoring system of a bridge, establishing the correlation model between temperature and temperature-induced deflection is meaningful. It is difficult to complete a high-quality model only by the girder temperature. The temperature features based on prior knowledge from the mechanical mechanism are used as the input information in this paper. At the same time, to strengthen the nonlinear ability of the model, this paper selects an independent recurrent neural network (IndRNN) for modeling. The deep learning neural network is compared with machine learning neural networks to prove the advancement of deep learning. When only the average temperature of the main girder is input, the calculation accuracy is not high regardless of whether the deep learning network or the machine learning network is used. When the temperature information extracted by the prior knowledge is input, the average error of IndRNN model is only 2.53%, less than those of BPNN model and traditional RNN. Combining knowledge with deep learning is undoubtedly the best modeling scheme. The deep learning model can provide a comparison value of bridge deformation for bridge management.

A modified brain emotional learning model for earthquake magnitude and fear prediction

2017 ◽  
Vol 34 (2) ◽  
pp. 261-276 ◽  
Author(s):  
Seyyedeh Hoora Fakhrmoosavy ◽  
Saeed Setayeshi ◽  
Arash Sharifi
Keyword(s):  
Learning Model ◽  
Earthquake Magnitude ◽  
Emotional Learning ◽  
Brain Emotional Learning
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