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|Title:||Neural approach to fuzzy modeling||Authors:||Nie, Junhong||Issue Date:||1994||Citation:||Nie, Junhong (1994). Neural approach to fuzzy modeling. Proceedings of the American Control Conference 2 : 2139-2143. ScholarBank@NUS Repository.||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.||Source Title:||Proceedings of the American Control Conference||URI:||http://scholarbank.nus.edu.sg/handle/10635/72772||ISSN:||07431619|
|Appears in Collections:||Staff Publications|
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