On the design of neural-fuzzy control system

This article presents a neural-network-based fuzzy logic control (NN-FLC) system. The NN-FLC model has the learning capabilities for constructing membership functions and extracting fuzzy rules from training examples. Both unsupervised and supervised training algorithms are used to find the membership functions of the FLC. Competitive learning algorithms are employed to evaluate fuzzy logic rules. Matlab programs using both neural and fuzzy toolboxes are developed to implement the NTST-FLC model. Computer simulations of the inverted pendulum controlled by NN-FLC system were conducted to illustrate the self-learning ability of the network. © 1998 John Wiley & Sons, Inc.link_to_subscribed_fulltex