Observer Design Based on Self-Recurrent Consequent-Part Fuzzy Wavelet Neural Network

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	Observer Design Based on Self-Recurrent Consequent-Part Fuzzy Wavelet Neural Network
其他题名	Observer Design Based on Self-Recurrent Consequent-Part Fuzzy Wavelet Neural Network
	Wen Xin 1; Li Xin 2
	2016
发表期刊	TSINGHUA SCIENCE AND TECHNOLOGY
ISSN	1007-0214
卷号	21 期号:5 页码:544-551
摘要	In this paper, we propose and construct an observer design based on a Self-Recurrent Consequent-Part Fuzzy Wavelet Neural Network (SRCPFWNN) for a class of nonlinear system. We use a Self-Recurrent Wavelet Neural Network (SRWNN) to construct a self-recurrent consequent part for each rule of the Takagi-Sugeno-Kang (TSK) model in the SRCPFWNN and analyze the structure of the fuzzy wavelet neural network model. Based on the Direct Adaptive Control Theory (DACT) and a back propagation-based learning algorithm, all parameters of the consequent parts are updated online in the SRCPFWNN. On this basis, we propose a design method using an adaptive state observer based on an SRCPFWNN for nonlinear systems. Using the Lyapunov function, we then prove the stability of this observer design method. Our simulation results confirm that the observer can accurately and quickly estimate the state values of the system.
其他摘要	In this paper, we propose and construct an observer design based on a Self-Recurrent Consequent-Part Fuzzy Wavelet Neural Network (SRCPFWNN) for a class of nonlinear system. We use a Self-Recurrent Wavelet Neural Network (SRWNN) to construct a self-recurrent consequent part for each rule of the Takagi-Sugeno-Kang (TSK) model in the SRCPFWNN and analyze the structure of the fuzzy wavelet neural network model. Based on the Direct Adaptive Control Theory (DACT) and a back propagation-based learning algorithm, all parameters of the consequent parts are updated online in the SRCPFWNN. On this basis, we propose a design method using an adaptive state observer based on an SRCPFWNN for nonlinear systems. Using the Lyapunov function, we then prove the stability of this observer design method. Our simulation results confirm that the observer can accurately and quickly estimate the state values of the system.
关键词	NONLINEAR-SYSTEMS STATE OBSERVER CONTROLLER FEEDBACK HYBRID Takagi-Sugeno-Kang (TSK) fuzzy model activation functions state observer nonlinear systems simulation
收录类别	CSCD
语种	英语
CSCD记录号	CSCD:5827683
引用统计	被引频次：1[CSCD] [CSCD记录]
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/147873
专题	中国科学院金属研究所
作者单位	1.中国科学院金属研究所 2.南京大学
推荐引用方式 GB/T 7714	Wen Xin,Li Xin. Observer Design Based on Self-Recurrent Consequent-Part Fuzzy Wavelet Neural Network[J]. TSINGHUA SCIENCE AND TECHNOLOGY,2016,21(5):544-551.
APA	Wen Xin,&Li Xin.(2016).Observer Design Based on Self-Recurrent Consequent-Part Fuzzy Wavelet Neural Network.TSINGHUA SCIENCE AND TECHNOLOGY,21(5),544-551.
MLA	Wen Xin,et al."Observer Design Based on Self-Recurrent Consequent-Part Fuzzy Wavelet Neural Network".TSINGHUA SCIENCE AND TECHNOLOGY 21.5(2016):544-551.