A hierarchical fuzzy-clustering approach to fuzzy modeling

Tsekouras, G; Sarimveis, H; Kavakli, E; Bafas, G

dc.contributor.author	Tsekouras, G	en
dc.contributor.author	Sarimveis, H	en
dc.contributor.author	Kavakli, E	en
dc.contributor.author	Bafas, G	en
dc.date.accessioned	2014-03-01T01:21:43Z
dc.date.available	2014-03-01T01:21:43Z
dc.date.issued	2005	en
dc.identifier.issn	0165-0114	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/16339
dc.subject	Fuzzy basis functions	en
dc.subject	Nearest neighbor clustering	en
dc.subject	Optimal fuzzy clustering	en
dc.subject	Ordinary fuzzy partitions	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.classification	Mathematics, Applied	en
dc.subject.classification	Statistics & Probability	en
dc.subject.other	Algorithms	en
dc.subject.other	Data reduction	en
dc.subject.other	Error analysis	en
dc.subject.other	Estimation	en
dc.subject.other	Membership functions	en
dc.subject.other	State space methods	en
dc.subject.other	Fuzzy basis functions	en
dc.subject.other	Nearest neighbor clustering	en
dc.subject.other	Optimal fuzzy clustering	en
dc.subject.other	Ordinary fuzzy partitions	en
dc.subject.other	Fuzzy sets	en
dc.title	A hierarchical fuzzy-clustering approach to fuzzy modeling	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.fss.2004.04.013	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.fss.2004.04.013	en
heal.language	English	en
heal.publicationDate	2005	en
heal.abstract	This paper introduces a new method for fuzzy modeling based on a hierarchical fuzzy-clustering scheme. The method consists of a sequence of steps aiming towards developing a Takagi-Sugeno (TS) fuzzy model of optimal structure, where the fuzzy sets in the premise part are of Gaussian type. Starting from an initial ordinary fuzzy partition of the input space, the algorithm performs a nearest-neighbor search and groups the original input training data into a number of clusters. The centers of these clusters are further processed using an optimal fuzzy clustering technique, which is based on the weighted fuzzy c-means algorithm. The resulted optimal fuzzy partition defines the number of fuzzy rules and provides an initial estimation for the system parameters, which in a next step are fine tuned using the well-known gradient-descend algorithm. The proposed method is successfully applied to three test examples where the produced fuzzy models prove to be very accurate, as well as compact in size. (C) 2004 Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	Fuzzy Sets and Systems	en
dc.identifier.doi	10.1016/j.fss.2004.04.013	en
dc.identifier.isi	ISI:000226447500004	en
dc.identifier.volume	150	en
dc.identifier.issue	2	en
dc.identifier.spage	245	en
dc.identifier.epage	266	en