Abstract
This paper describes a general and systematic approach to constructing a multivariable fuzzy model from numerical data through a self-organizing counterpropagation network (SOCPN). Two self-organizing algorithms USOCPN and SSOCPN, being unsupervised and supervised respectively, 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 use of the SOCPN is as an online adaptive fuzzy model in which the rule-base in terms of connection weights is updated successively in response to the incoming measured data. The comparative results on three well studied examples suggest that the method has merits of simple structure, fast learning speed, and good modeling accuracy.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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