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 behaviour: 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 behaviour, 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.
Available at: http://works.bepress.com/thomas_sudkamp/19/
Presented at the Third International Symposium on Uncertainty Modeling and Analysis, 1995, and Annual Conference of the North American Fuzzy Information Processing Society. Proceedings of ISUMA - NAFIPS '95, College Park, MD.