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Dynamic adaptive learning algorithm based on two-fuzzy neural-networks
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DOI：10.1016/j.neucom.2012.07.048.png)
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Abstract
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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. (C) 2013 Elsevier B.V. All rights reserved.
Keywords:
Dynamic adaptive learning algorithm
Fuzzy rules
Fuzzy neural network
Partially unknown nonlinear control system
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