Neural fuzzy relational systems with a new learning algorithm

Stamou, GB; Tzafestas, SG

dc.contributor.author	Stamou, GB	en
dc.contributor.author	Tzafestas, SG	en
dc.date.accessioned	2014-03-01T01:15:44Z
dc.date.available	2014-03-01T01:15:44Z
dc.date.issued	2000	en
dc.identifier.issn	0378-4754	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/13691
dc.subject	fuzzy relational equations	en
dc.subject	neural network	en
dc.subject	triangular norm	en
dc.subject	neural fuzzy systems	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Computer Science, Software Engineering	en
dc.subject.classification	Mathematics, Applied	en
dc.subject.other	RELATION EQUATIONS	en
dc.subject.other	TRIANGULAR NORMS	en
dc.subject.other	INFERENCE	en
dc.title	Neural fuzzy relational systems with a new learning algorithm	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/S0378-4754(99)00126-3	en
heal.identifier.secondary	http://dx.doi.org/10.1016/S0378-4754(99)00126-3	en
heal.language	English	en
heal.publicationDate	2000	en
heal.abstract	Fuzzy relational systems can represent symbolic knowledge in a formal numerical (subsymbolic) framework, with the aid of fuzzy relation equations. The disadvantage of this methodology is the need for a priori knowledge in order to construct the fuzzy relation equation. In this paper, a neural network model is proposed in order to represent fuzzy relational systems without the need of the construction of the fuzzy relation equation. The network ensures the ideal perfect recall of fuzzy associative memories when the a posteriori constructed fuzzy relation equation has a non-empty solution set. It is actually a single layer of generalized neurons (compositional neurons) that perform the sup-t-norm composition, An on-line learning algorithm adapting the weights of its interconnections is incorporated into the neural network. These weights are actually the elements of the fuzzy relation representing the fuzzy relational system. The algorithm is based on the knowledge about the topographic structure of the respective fuzzy relation. (C) 2000 IMACS/Elsevier Science B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	MATHEMATICS AND COMPUTERS IN SIMULATION	en
dc.identifier.doi	10.1016/S0378-4754(99)00126-3	en
dc.identifier.isi	ISI:000084223700014	en
dc.identifier.volume	51	en
dc.identifier.issue	3-4	en
dc.identifier.spage	301	en
dc.identifier.epage	314	en