A connectionist model for weighted fuzzy programs

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	2014-03-01T02:43:48Z
dc.date.available	2014-03-01T02:43:48Z
dc.date.issued	2006	en
dc.identifier.issn	10987576	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31512
dc.subject	connectionist models	en
dc.subject	Fuzzy Logic Programming	en
dc.subject	Fuzzy Programming	en
dc.subject	Logic Programs	en
dc.subject	Neural Network	en
dc.subject.other	Dynamical systems	en
dc.subject.other	Logic programming	en
dc.subject.other	Mathematical models	en
dc.subject.other	Neural networks	en
dc.subject.other	Uncertain systems	en
dc.subject.other	Fuzzy logic programming	en
dc.subject.other	Herbrand model	en
dc.subject.other	Weighted fuzzy programs	en
dc.subject.other	Fuzzy logic	en
dc.title	A connectionist model for weighted fuzzy programs	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/IJCNN.2006.247265	en
heal.identifier.secondary	http://dx.doi.org/10.1109/IJCNN.2006.247265	en
heal.identifier.secondary	1716514	en
heal.publicationDate	2006	en
heal.abstract	The usefulness of the results of logic programming in real-life applications is sometimes limited due to the inability of this theory to model the uncertain and dynamic character of real environments. Fuzzy logic programming has been lately considered as an important framework for handling uncertainty in logic programming systems. Still, there is a need for modelling adaptation of logic programs and the progress in this area is rather slow. In the present paper, we first extend fuzzy logic programs in a direction that brings them closer to the connectionist approach: we introduce weighted fuzzy programs, which allow the association of significance weights with the atoms that make up the body of a logic rule. The weights add expressiveness to the programs and allow the determination of the degree with which an antecedent affects the value of the rule consequent. Then, we propose a neural network implementation of weighted fuzzy programs that is capable of computing the minimal Herbrand model of a weighted fuzzy program. © 2006 IEEE.	en
heal.journalName	IEEE International Conference on Neural Networks - Conference Proceedings	en
dc.identifier.doi	10.1109/IJCNN.2006.247265	en
dc.identifier.spage	3055	en
dc.identifier.epage	3062	en