Adaptation of connectionist weighted fuzzy logic programs with Kripke-Kleene semantics

Chortaras, A; Stamou, G; Stafylopatis, A; Kollias, S

dc.contributor.author	Chortaras, A	en
dc.contributor.author	Stamou, G	en
dc.contributor.author	Stafylopatis, A	en
dc.contributor.author	Kollias, S	en
dc.date.accessioned	2014-03-01T02:45:05Z
dc.date.available	2014-03-01T02:45:05Z
dc.date.issued	2008	en
dc.identifier.issn	03029743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32138
dc.subject	Adaptive Algorithm	en
dc.subject	Fuzzy Logic Programming	en
dc.subject	Fuzzy Rules	en
dc.subject	Learning Algorithm	en
dc.subject.other	Adaptation algorithms	en
dc.subject.other	Data sets	en
dc.subject.other	Fuzzy-logics	en
dc.subject.other	Negation as failures	en
dc.subject.other	Subgradient descent	en
dc.subject.other	Backpropagation	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Learning algorithms	en
dc.subject.other	Logic programming	en
dc.subject.other	Neural networks	en
dc.subject.other	Semantics	en
dc.subject.other	Fuzzy logic	en
dc.title	Adaptation of connectionist weighted fuzzy logic programs with Kripke-Kleene semantics	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-3-540-87536-9_51	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-540-87536-9_51	en
heal.publicationDate	2008	en
heal.abstract	Weighted fuzzy logic programs extend the expressiveness of fuzzy logic programs by allowing the association of a different significance weight with each atom that appears in the body of a fuzzy rule. The semantics and a connectionist representation of these programs have already been studied in the absence of negation; in this paper we first propose a Kripke-Kleene based semantics for the programs which allows for the use of negation as failure. Taking advantage of the increased modelling capabilities of the extended programs, we then describe their connectionist representation and study the problem of adapting the rule weights in order to fit a provided dataset. The adaptation algorithm we develop is based on the subgradient descent method and hence is appropriate to be employed as a learning algorithm for the training of the connectionist representation of the programs. © Springer-Verlag Berlin Heidelberg 2008.	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
dc.identifier.doi	10.1007/978-3-540-87536-9_51	en
dc.identifier.volume	5163 LNCS	en
dc.identifier.issue	PART 1	en
dc.identifier.spage	492	en
dc.identifier.epage	502	en