Title :
Radial basis function based adaptive fuzzy systems
Author :
Cho, Kwang Bo ; Wang, Bo Hyeun
Author_Institution :
Goldstar Central Res. Lab., Seoul, South Korea
Abstract :
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
Keywords :
adaptive systems; feedforward neural nets; fuzzy systems; inference mechanisms; Gaussian functions; architectural deviations; classification; fuzzy IF-THEN rules; input sample data; membership functions; output sample data; radial basis function based adaptive fuzzy systems; time series prediction; Adaptive systems; Data mining; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Inference algorithms; Laboratories; Modeling; Neural networks; Radial basis function networks;
Conference_Titel :
Fuzzy Systems, 1995. International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium., Proceedings of 1995 IEEE Int
Conference_Location :
Yokohama
Print_ISBN :
0-7803-2461-7
DOI :
10.1109/FUZZY.1995.409688
