Dynamic adaptive learning algorithm based on two-fuzzy neural-networks

Authors:
Dan Meng;Zheng Pei
Affiliations:
School of Economics Information Engineering, Southwestern University of Finance and Economics, Chengdu 611130, China;School of Mathematics & Computer Engineering, Xihua University, Chengdu 610039, China
Venue:
Neurocomputing
Year:
2014

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Abstract

A dynamic adaptive learning algorithm based on two fuzzy neural-networks for the control of a partially unknown nonlinear dynamic system is developed in this paper. The proposed fuzzy neural-network controller is composed of a computation controller and a learning controller. The computation controller and a learning controller will control collaboratively for partially unknown nonlinear dynamic system. Formally, the stability of the control system and convergence of the fuzzy neural-network have been proved. The proposed algorithm based on two fuzzy neural-networks can avoid the time-consuming trial-and-error tuning procedure for determining structure and parameters. The simulation experiment shows that the proposed method is feasible, valid and rational.