dc.contributor.authorSong, Hengjieen_US
dc.date.accessioned2014-04-07T10:25:22Z
dc.date.available2014-04-07T10:25:22Z
dc.date.copyright2010en_US
dc.date.issued2010
dc.identifier.urihttp://hdl.handle.net/10356/57401
dc.description176 p.en_US
dc.description.abstractFuzzy Cognitive Maps (FCMs) are convenient and widely used technique for modelling dynamic systems, which are characterized by flexibility and adaptability to a given domain. However since lacking efficient methods to automatically identify the membership functions and to quantify the causalities which are the very foundations of FCM theory, constructing FCMs for complex systems heavily relies on expert knowledge. The manually developed models have a substantial shortcoming due to the model subjectivity and difficulties in evaluating its reliability.en_US
dc.rightsNanyang Technological Universityen_US
dc.subjectDRNTU::Engineering::Computer science and engineering::Theory of computation::Analysis of algorithms and problem complexityen_US
dc.titleLearning algorithms in fuzzy cognitive mapsen_US
dc.typeThesisen_US
dc.contributor.supervisorMiao Chun Yanen_US
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.description.degreeDoctor of Philosophy (SCE)en_US


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