Neural approach to fuzzy modeling

Abstract

This paper is concerned with the problem of constructing a fuzzy model from numerical data through a self-organizing counterpropagation network (SOCPN). Two self-organizing algorithms, unsupervised USOCPN and supervised SSOCPN, are introduced. SOCPN can be employed in two ways. In the first place, it can be used as a knowledge extractor by which a set of rules are generated from the available numerical data set. The generated rule-base is then utilized by a fuzzy reasoning model. The second usage is to use the SOCPN as an on-line adaptive fuzzy model in which the rule-base in terms of connection weights is updated successively in response to the incoming measured data. The simulation results on some well studied examples are given.