Radial basis function based adaptive fuzzy systems

This paper describes a fuzzy system with adaptive capability to extract fuzzy IF-THEN rules from input and output sample data. The proposed system, called radial basis function (RBF) based adaptive fuzzy system (AFS), employs the Gaussian functions to represent the membership functions of the premise part of fuzzy rules. Three architectural deviations of the RBF based APS are also presented according to different consequence types. These provide versatility of the network to handle arbitrary fuzzy inference schemes. We present examples of classification and time series prediction to illustrate how to solve these problems using the RBF based AFS. We also compare the results of our approach with those of others to demonstrate its validity and effectiveness