Connectionist weighted fuzzy logic programs

Chortaras, A; Stamou, G; Stafylopatis, A

dc.contributor.author	Chortaras, A	en
dc.contributor.author	Stamou, G	en
dc.contributor.author	Stafylopatis, A	en
dc.date.accessioned	2014-03-01T02:45:12Z
dc.date.available	2014-03-01T02:45:12Z
dc.date.issued	2008	en
dc.identifier.issn	0925-2312	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32199
dc.subject	Connectionist-symbolic integration	en
dc.subject	Fuzzy logic programming	en
dc.subject	Imperfect knowledge representation	en
dc.subject	Rule adaptation	en
dc.subject	Subgradient descent learning	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.other	Computer programming	en
dc.subject.other	Education	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Fuzzy systems	en
dc.subject.other	Information theory	en
dc.subject.other	Knowledge representation	en
dc.subject.other	Learning algorithms	en
dc.subject.other	Learning systems	en
dc.subject.other	Logic programming	en
dc.subject.other	Connectionist-symbolic integration	en
dc.subject.other	Fuzzy logic programming	en
dc.subject.other	Imperfect knowledge representation	en
dc.subject.other	Rule adaptation	en
dc.subject.other	Subgradient descent learning	en
dc.subject.other	Fuzzy logic	en
dc.subject.other	analytical error	en
dc.subject.other	artificial neural network	en
dc.subject.other	conceptual framework	en
dc.subject.other	conference paper	en
dc.subject.other	controlled study	en
dc.subject.other	fuzzy logic	en
dc.subject.other	knowledge	en
dc.subject.other	learning algorithm	en
dc.subject.other	machine learning	en
dc.subject.other	mathematical computing	en
dc.subject.other	normal distribution	en
dc.subject.other	parametric test	en
dc.subject.other	priority journal	en
dc.subject.other	probability	en
dc.subject.other	semantics	en
dc.title	Connectionist weighted fuzzy logic programs	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1016/j.neucom.2007.11.034	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.neucom.2007.11.034	en
heal.language	English	en
heal.publicationDate	2008	en
heal.abstract	Fuzzy logic programs are a useful framework for imperfect knowledge representation and reasoning using the formalism of logic programming. Nevertheless, there is the need for modeling adaptation of fuzzy logic programs, so that machine learning techniques, such as connectionist-based learning, can be applied. Weighted fuzzy logic programs bring fuzzy logic programs and connectionist models closer together by associating a significant weight with each atom in the body of a fuzzy rule: by exploiting the existence of the weights, it is possible to construct a connectionist model that reflects the exact structure of a weighted fuzzy logic program. Based on the connectionist representation, we first define the weight adaptation problem as the task of adapting the weights of the rules of a weighted fuzzy logic program, so that they fit: best a set of training data, and then we develop a subgradient descent learning algorithm for the connectionist model that allows us to obtain an approximate solution for the weight adaptation problem. (C) 2008 Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	Neurocomputing	en
dc.identifier.doi	10.1016/j.neucom.2007.11.034	en
dc.identifier.isi	ISI:000259121100006	en
dc.identifier.volume	71	en
dc.identifier.issue	13-15	en
dc.identifier.spage	2456	en
dc.identifier.epage	2469	en