Fuzzy non-homogeneous Markov systems

Symeonaki, MA; Stamou, GB; Tzafestas, SG

dc.contributor.author	Symeonaki, MA	en
dc.contributor.author	Stamou, GB	en
dc.contributor.author	Tzafestas, SG	en
dc.date.accessioned	2014-03-01T01:17:56Z
dc.date.available	2014-03-01T01:17:56Z
dc.date.issued	2002	en
dc.identifier.issn	0924-669X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/14709
dc.subject	Fuzzy logic	en
dc.subject	Fuzzy Markov system	en
dc.subject	Fuzzy reasoning	en
dc.subject	Non-homogeneous Markov systems	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.other	Asymptotic stability	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Logic programming	en
dc.subject.other	Markov processes	en
dc.subject.other	Probability distributions	en
dc.subject.other	Uncertain systems	en
dc.subject.other	Fuzzy logic	en
dc.subject.other	Fuzzy Markov systems	en
dc.subject.other	Fuzzy reasoning	en
dc.subject.other	Nonhomogeneous Markov systems	en
dc.subject.other	Expert systems	en
dc.title	Fuzzy non-homogeneous Markov systems	en
heal.type	journalArticle	en
heal.identifier.primary	10.1023/A:1016164915513	en
heal.identifier.secondary	http://dx.doi.org/10.1023/A:1016164915513	en
heal.language	English	en
heal.publicationDate	2002	en
heal.abstract	In this paper the theory of fuzzy logic and fuzzy reasoning is combined with the theory in Markov systems and the concept of a fuzzy non-homogeneous Markov system is introduced for the first time. This is an effort to deal with the uncertainty introduced in the estimation of the transition probabilities and the input probabilities in Markov systems. The asymptotic behaviour of the fuzzy Markov system and its asymptotic variability is considered and given in closed analytic form. Moreover, the asymptotically attainable structures of the system are estimated also in a closed analytic form under some realistic assumptions. The importance of this result lies in the fact that in most cases the traditional methods for estimating the probabilities can not be used due to lack of data and measurement errors. The introduction of fuzzy logic into Markov systems represents a powerful tool for taking advantage of the symbolic knowledge that the experts of the systems possess.	en
heal.publisher	KLUWER ACADEMIC PUBL	en
heal.journalName	Applied Intelligence	en
dc.identifier.doi	10.1023/A:1016164915513	en
dc.identifier.isi	ISI:000176762400005	en
dc.identifier.volume	17	en
dc.identifier.issue	2	en
dc.identifier.spage	203	en
dc.identifier.epage	214	en