Continuous optimization schemes for fuzzy classification

Blekas, K; Papageorgiou, G; Stafylopatis, A

dc.contributor.author	Blekas, K	en
dc.contributor.author	Papageorgiou, G	en
dc.contributor.author	Stafylopatis, A	en
dc.date.accessioned	2014-03-01T02:41:19Z
dc.date.available	2014-03-01T02:41:19Z
dc.date.issued	1997	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30459
dc.subject	Continuous Optimization	en
dc.subject	Fuzzy Classification	en
dc.subject	Genetics	en
dc.subject	Neural Network	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Neural networks	en
dc.subject.other	Optimization	en
dc.subject.other	Fuzzy classification scheme	en
dc.subject.other	Hopfield neural networks	en
dc.subject.other	Pattern recognition	en
dc.title	Continuous optimization schemes for fuzzy classification	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICDSP.1997.628057	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICDSP.1997.628057	en
heal.publicationDate	1997	en
heal.abstract	Two approaches are developed, which are suitable for the optimization of a fuzzy classification scheme through the formation of appropriate space-filling clusters. The first approach is based on the analog Hopfield neural network, while the second one uses real-encoded genetic optimization. Experimental results concerning difficult classification problems show that both proposed approaches are very successful in generating fuzzy partitions and outperform other known algorithm in terms of the correct placement of patterns into partitions.	en
heal.publisher	IEEE, Piscataway, NJ, United States	en
heal.journalName	International Conference on Digital Signal Processing, DSP	en
dc.identifier.doi	10.1109/ICDSP.1997.628057	en
dc.identifier.volume	1	en
dc.identifier.spage	265	en
dc.identifier.epage	268	en