Graph coloring using fuzzy controlled neural networks

Dalianis, P; Kitsios, Y; Tzafestas, S

dc.contributor.author	Dalianis, P	en
dc.contributor.author	Kitsios, Y	en
dc.contributor.author	Tzafestas, S	en
dc.date.accessioned	2014-03-01T01:13:47Z
dc.date.available	2014-03-01T01:13:47Z
dc.date.issued	1998	en
dc.identifier.issn	1079-8587	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/12720
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-0006766869&partnerID=40&md5=7f9bde5f77ff340ce8b57957809a30b2	en
dc.subject	Fuzzy controlled neural network	en
dc.subject	Graph coloring	en
dc.subject	Neural networks	en
dc.subject	Neurofuzzy k-coloring algorithms	en
dc.subject.classification	Automation & Control Systems	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.other	OPTIMIZATION	en
dc.subject.other	STABILITY	en
dc.title	Graph coloring using fuzzy controlled neural networks	en
heal.type	journalArticle	en
heal.language	English	en
heal.publicationDate	1998	en
heal.abstract	Graph coloring is an important example of a class of combinatorial optimization problems, which are characterized by their large number of interacting degrees of freedom. Neural Networks (NN) and especially a variation of the Hopfield one constitutes a very popular approach to the solution of problems in this class. The algorithm presented here is based on the idea of improving the convergence performance of such a NN, with the use of a simple fuzzy controller. The algorithm is explained and applied to two graphs of different size. The simulation results are presented and described in detail.	en
heal.publisher	AUTOSOFT PRESS	en
heal.journalName	Intelligent Automation and Soft Computing	en
dc.identifier.isi	ISI:000077286000001	en
dc.identifier.volume	4	en
dc.identifier.issue	4	en
dc.identifier.spage	273	en
dc.identifier.epage	288	en