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dc.contributor.authorAkın, Ömer
dc.contributor.authorBayeğ, Selami
dc.date.accessioned2020-06-26T13:08:40Z
dc.date.available2020-06-26T13:08:40Z
dc.date.issued2016
dc.identifier.citationAkın, Ö. and Doğan, N. (2016).An Indicator Operator Algorihm For solving A Second Order Intuitionistic Fuzzy Initial Value. Problem.International Conference On Mathematics And Mathematics Educaton(ICMME-2016)May 12 – 14,2016,Elazığ/Turkeyen_US
dc.identifier.urihttp://hdl.handle.net/20.500.11851/3565
dc.identifier.urihttp://theicmme.org/docs/Abstracts_Book/ICMME-2016_ABSTRACTS_BOOK.pdf
dc.descriptionInternational Conference On Mathematics And Mathematics Educatİon (ICMME-2016) (12-14 May 2016: Elazığ, Turkey)en_US
dc.description.abstractL. Zadeh [1] was the first who introduced the concept of fuzzy settheory as an extension of the classical notion of the set theory. He remindedpeople that things are not always black or white; there may besome grey colours in life. Hence he simply assigned so called the membershipfunction to each element in a classical set and started the fuzzyset theory. However in some cases the membership concept in a fuzzyset is itself uncertain. This uncertainty may be because of the subjectivityof expert knowledge, complexity of data or imprecision of themodels. Since the fuzzy set theory considers only the degree of membership,it does not involve the degree of uncertainty for the membership.To handle such situations, the generalized concepts of fuzzy set theory are used [1]-[5]. One of these generalizations is intuitionistic fuzzy settheory which was given by Atanassov [2]. Atanassov introduced intuitionisticfuzzy set concept by extending the definition of fuzzy set afteranalyzing the shortcomings of it. He defined the intuitionistic fuzzy setconcept by introducing the the nonmembership function into the fuzzyset such that sum of both is less than one. And in his further researches,he showed the exclusive properties of intuitionistic fuzzy sets [6]-[13].In the past decades, the intuitionistic fuzzy set theory has penetratedinto different research areas, such as decision making [14]-[17], clusteringanalysis [18], medical diagnosis [19]-[20], pattern recognition [21]-[23] In recent years the topic of fuzzy differential equations has beenrapidly grown to model the real life situations where the observed datais insufficient [24]-[27]. Especially to describe the relation between velocityand acceleration, second order differential equations has greaterimportance in science. Therefore many approaches [27]-[29] were givento solve second order fuzzy differential equations. However there areonly few works [30]-[32] to observe the intuitionistic fuzzy differentialequations.In this work, we have examined the solution of the following second orderintuitionistic fuzzy initial value problems given in Eq. (1)-(2) usingintuitionistic Zadeh’s Extension Principle [13]. 𝑦 ′′(𝑥) + 𝑎1 𝑖 𝑦 ′ (𝑥) + 𝑎2 𝑖 𝑦(𝑥) = ∑𝑏 ̅𝑗 𝑖𝑔𝑗(𝑥) 𝑟 𝑗=1 𝑦(0) = 𝛾 ̅0 𝑖 ; 𝑦′(0) = 𝛾 ̅1 𝑖 Here 𝛾 ̅0 𝑖 ; 𝛾 ̅1 𝑖 and 𝑏̅ 𝑗 𝑖 (j=1, 2, 3,…, r ) are intuitionistic fuzzy numbers. 𝑔𝑗(𝑥) (j=1, 2, 3,…,r ) are continuous forcing functions on the interval [0, ∞).en_US
dc.language.isoengen_US
dc.publisherMatematikçiler Derneğien_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectIntuitionistic fuzzy setsen_US
dc.subjectZadeh’s extension principleen_US
dc.subjectIntuitionistic fuzzy differential equationsen_US
dc.titleAn Indicator Operator Algorithm for Solving a Second Order Intuitionistic Fuzzy Initial Value Problemen_US
dc.typeconferenceObjecten_US
dc.contributor.departmentTOBB ETÜ, Fen Edebiyat Fakültesi, Matematik Bölümütr_TR
dc.contributor.departmentTOBB ETU, Faculty of Science and Literature, Department of Mathematicsen_US
dc.identifier.startpage266en_US
dc.identifier.endpage268en_US
dc.contributor.tobbetuauthorAkın, Ömer
dc.contributor.YOKid5047
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıtr_TR


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