An adaptive hierarchical fuzzy model

Abstract Fuzzy models have been designed to represent approximate or imprecise relationships in complex systems and have been successfully employed in control systems, database systems and decision analysis. A hierarchical architecture for fuzzy modeling and inference has been developed to allow adaptation based on 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 continued learning, the underlying system does not change and the adaptive algorithm utilizes the real-time data and associated feedback to improve the accuracy of the existing model. Gradual and drastic change represent fundamental alterations to the system being modeled. 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.