Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/72772
Title: Neural approach to fuzzy modeling
Authors: Nie, Junhong 
Issue Date: 1994
Source: 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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