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This paper presents a wavelet fuzzy neural network (WFNN) structure for identifying and controlling nonlinear dynamic systems. The proposed WFNN is constructed on the base of a set of fuzzy rules. Each rule includes a wavelet function in the consequent part of the rules. A training algorithm adopting a gradient descent method is employed to identify the unknown parameters in the WFNN. For the control...
In this paper, an indirect adaptive control scheme based on Takagi-Sugeno (TS)-type recurrent fuzzy models is proposed for nonlinear plants with unmeasurable states. The TS-type recurrent fuzzy model is used as the dynamic model of the nonlinear plant. Its recurrent property comes from that it can memorize temporal information with the feedback connections between its states layer and inputs layer,...
This paper presents a fuzzy neural-based filtered-X least-mean-square (LMS) algorithm for active noise control (ANC) system. The saturation of the power amplifier in ANC system is considered. A method for compensating the saturation is proposed. An on line dynamic learning algorithm based on the error gradient descent method is carried out. The convergence of the algorithm is proven using a discrete...
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