Knowledge refinement using fuzzy compositional neural networks

Tzouvaras, V; Stamou, G; Kollias, S

dc.contributor.author	Tzouvaras, V	en
dc.contributor.author	Stamou, G	en
dc.contributor.author	Kollias, S	en
dc.date.accessioned	2014-03-01T01:19:05Z
dc.date.available	2014-03-01T01:19:05Z
dc.date.issued	2003	en
dc.identifier.issn	0302-9743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/15375
dc.subject	Composition Operator	en
dc.subject	Fuzzy Relation	en
dc.subject	Learning Algorithm	en
dc.subject	Neural Network	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.other	ARCHIMEDEAN TRIANGULAR NORMS	en
dc.subject.other	RELATION EQUATIONS	en
dc.title	Knowledge refinement using fuzzy compositional neural networks	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/3-540-44989-2_111	en
heal.identifier.secondary	http://dx.doi.org/10.1007/3-540-44989-2_111	en
heal.language	English	en
heal.publicationDate	2003	en
heal.abstract	Fuzzy relations as representational tools and fuzzy compositional operators as reasoning components, are user in this paper in order to represent knowledge expressed in semantic rules. Furthermore, neural representation and resolution of composite fuzzy relation equations provides knowledge refinement and adaptation to a specific context. A two-layer fuzzy compositional neural network is proposed in this work, with a learning algorithm changing the weights and minimize the error of the small context changes. © Springer-Verlag Berlin Heidelberg 2003.	en
heal.publisher	SPRINGER-VERLAG BERLIN	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
heal.bookName	LECTURE NOTES IN COMPUTER SCIENCE	en
dc.identifier.doi	10.1007/3-540-44989-2_111	en
dc.identifier.isi	ISI:000185378100111	en
dc.identifier.volume	2714	en
dc.identifier.spage	933	en
dc.identifier.epage	940	en