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Open Access

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

Xin Wen( )Xin Li
Department of Aeronautics and Astronautics, Shenyang Aerospace University, Shenyang 110136, China.
School Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
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Abstract

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.

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Tsinghua Science and Technology
Pages 544-551
Cite this article:
Wen X, Li X. Observer Design Based on Self-Recurrent Consequent-Part Fuzzy Wavelet Neural Network. Tsinghua Science and Technology, 2016, 21(5): 544-551. https://doi.org/10.1109/TST.2016.7590323

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Received: 21 July 2015
Revised: 30 January 2016
Accepted: 26 May 2016
Published: 18 October 2016
© The author(s) 2016
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