Abstract

Fuzzy models have been designed to represent approximate or imprecise relationships in complex systems and have been successfully employed in control systems, expert systems, and decision analysis. A hierarchical architecture for fuzzy modeling and inference has been developed to learn an initial set of rules from training data and allow adaptation of the rule base via system performance feedback. A general adaptive algorithm is presented and its performance examined for three types of adaptive behavior: continued learning, gradual change, and drastic change. In each of the three types of behavior, the adaptive algorithm has been shown to be able to reconfigure the rule bases to either improve the original approximation or adapt to the new system.

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