Fuzzy compositional modeling

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dc.contributor.author Fu, Xin
dc.contributor.author Shen, Qiang
dc.date.accessioned 2010-08-11T10:29:48Z
dc.date.available 2010-08-11T10:29:48Z
dc.date.issued 2010-08
dc.identifier.citation Fu , X & Shen , Q 2010 , ' Fuzzy compositional modeling ' IEEE Transactions on Fuzzy Systems , vol 18 , no. 4 , pp. 823-840 . , 10.1109/TFUZZ.2010.2050325 en
dc.identifier.issn 1063-6706
dc.identifier.other PURE: 150284
dc.identifier.other dspace: 2160/5163
dc.identifier.uri http://hdl.handle.net/2160/5163
dc.description Fu, X., Shen, Q. (2010). Fuzzy compositional modeling. IEEE Transactions on Fuzzy Systems, 18 (4), 823-840 Sponsorship: EPSRC en
dc.description.abstract Automated modeling refers to automatic (re-)formulation of alternative system models that embody the simplification, abstraction, and approximation of knowledge and data for a given task. This technique is highly desirable for effective problem solving in many application domains. Over the past two decades, compositional modeling (CM) has established itself as a leading approach in automated modeling. CM is a framework to construct system models by composing generic and reusable model fragments (MFs) selected from a knowledge base. However, the existing work mainly concerns the knowledge and data that are represented by crisp and precise information. Little work has been carried out to explore its potential to deal with uncertain environments. This paper presents an innovative framework of fuzzy compositional modeling (FCM) to develop such work. The proposed approach is capable of representing and reasoning with a wide range of inexact information. An innovative notion of fuzzy complex numbers (FCNs) is developed in an effort to enable synthesis of consistent scenario descriptions from imprecise MFs. This paper also introduces the modulus of FCNs to constrain the resulting scenario descriptions. The usefulness of this study is illustrated by means of an example to construct possible scenario descriptions from given evidence, which is in support of crime investigation. en
dc.format.extent 18 en
dc.language.iso eng
dc.relation.ispartof IEEE Transactions on Fuzzy Systems en
dc.title Fuzzy compositional modeling en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.identifier.doi http://dx.doi.org/10.1109/TFUZZ.2010.2050325
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Advanced Reasoning Group en
dc.description.status Peer reviewed en


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