Pattern recognition and control by adaptive methods for an intelligent mobile vehicle

This paper describes adaptive methods for both pattern recognition and control in an experimental mobile vehicle (MV). An adaptive resonance theory (ART) neural network is used as the character recognizer. It can self-organize and self-stabilize in response to complex binary input vectors. New input patterns can be saved in such a fashion that the stored patterns are not forgotten or destroyed. By merging the advantages of the feed-forward neural network, adaptive algorithm, and fuzzy control, a neuro-fuzzy system also is proposed. This can deal with a large amount of training data in the neural network, from these data produce more reasonable fuzzy rules with the adaptive algorithm, and then control the object by fuzzy control. This is not a simple combination of the three methods, but a merger into one intelligent control system. Finally, the experimental results and some conclusions are given.