Possibilistic evaluation of extended fuzzy rules in the presence of uncertainty

Wallace, M; Kollias, S

dc.contributor.author	Wallace, M	en
dc.contributor.author	Kollias, S	en
dc.date.accessioned	2014-03-01T02:43:28Z
dc.date.available	2014-03-01T02:43:28Z
dc.date.issued	2005	en
dc.identifier.issn	10987584	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31428
dc.subject	Fuzzy Number	en
dc.subject	Fuzzy Rule Base	en
dc.subject	Fuzzy Rules	en
dc.subject	Rule Based System	en
dc.subject.other	Information analysis	en
dc.subject.other	Knowledge based systems	en
dc.subject.other	Number theory	en
dc.subject.other	Problem solving	en
dc.subject.other	Uncertain systems	en
dc.subject.other	Antecedents	en
dc.subject.other	Fuzzy numbers	en
dc.subject.other	Fuzzy rules	en
dc.subject.other	Fuzzy sets	en
dc.title	Possibilistic evaluation of extended fuzzy rules in the presence of uncertainty	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/FUZZY.2005.1452499	en
heal.identifier.secondary	http://dx.doi.org/10.1109/FUZZY.2005.1452499	en
heal.publicationDate	2005	en
heal.abstract	Characterization fuzzy in term ""fuzzy rule base"" is currently referred to the ability to define rule antecedents using fuzzy numbers. On the other hand, when it comes to the knowledge described by the rules and to the information contained in rule antecedents, absolute accuracy is assumed. With the emergence of a vast variety of applications of rule based systems, where antecedents are not provided by sensors but rather by complicated processing modules, more efficient rules and rule evaluation structures are needed, that are able to describe knowledge in more intuitive manner and cope with uncertainty in the assumed input. In this paper we propose extended fuzzy rules that allow for optional antecedents and provide a methodology for the possibilistic evaluation of both conventional and extended fuzzy rules in the presence of uncertainty. The work has been successfully applied in a real life problem, for which conventional fuzzy rules and fuzzy rule evaluation were inadequate. © 2005 IEEE.	en
heal.journalName	IEEE International Conference on Fuzzy Systems	en
dc.identifier.doi	10.1109/FUZZY.2005.1452499	en
dc.identifier.spage	815	en
dc.identifier.epage	820	en