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Observer Design Based on Self-Recurrent Consequent-Part Fuzzy Wavelet Neural Network
Alternative TitleObserver Design Based on Self-Recurrent Consequent-Part Fuzzy Wavelet Neural Network
Wen Xin1; Li Xin2
2016
Source PublicationTSINGHUA SCIENCE AND TECHNOLOGY
ISSN1007-0214
Volume21Issue:5Pages:544-551
AbstractIn 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.
Other AbstractIn 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.
KeywordNONLINEAR-SYSTEMS STATE OBSERVER CONTROLLER FEEDBACK HYBRID Takagi-Sugeno-Kang (TSK) fuzzy model activation functions state observer nonlinear systems simulation
Indexed ByCSCD
Language英语
CSCD IDCSCD:5827683
Citation statistics
Document Type期刊论文
Identifierhttp://ir.imr.ac.cn/handle/321006/147873
Collection中国科学院金属研究所
Affiliation1.中国科学院金属研究所
2.南京大学
Recommended Citation
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.
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