%0 Journal Article
%T Observer Design Based on Self-Recurrent Consequent-Part Fuzzy Wavelet Neural Network
%A Xin Wen
%A Xin Li
%J 清华大学学报自然科学版（英文版）
%@ 1878-7606
%D 2016
%R 10.1109/TST.2016.7590323
%X 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.
%K Takagi-Sugeno-Kang (TSK) fuzzy model
%K activation functions
%K state observer
%K nonlinear systems
%K simulation
%U http://tst.tsinghuajournals.com/EN/10.1109/TST.2016.7590323