On developing an adaptive neural-fuzzy control system

An adaptive neural-fuzzy control scheme for intelligent control is proposed. The control system consists of a fuzzy-neural controller (FNC) and model neural network (MNN). In the FNC, the antecedence and consequence of the fuzzy rule are constructed by a clustering method and a multilayer neural network. In the MNN, a multilayer neural network is utilized to identify an unknown controlled plant. The error backpropagation algorithm has been adopted as a learning technique. The effectiveness of the scheme is demonstrated by computer simulations of a cart-pole and a two-d.o.f. robot manipulator.

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