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Please use this identifier to cite or link to this item: http://eprint.iitd.ac.in/handle/2074/2038

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dc.contributor.authorHanmandlu, M-
dc.contributor.authorMadasu, V K-
dc.contributor.authorVasikarla, S-
dc.date.accessioned2006-08-04T05:08:07Z-
dc.date.available2006-08-04T05:08:07Z-
dc.date.issued2003-
dc.identifier.citationInformation Technology: Coding and Computing Computers and Communications Proceedings ITCC International Conference on, 627 - 634p.en
dc.identifier.urihttp://eprint.iitd.ac.in/dspace/handle/2074/2038-
dc.description.abstractCluster-weighted modeling (CWM) is emerging as a versatile tool for modeling dynamical systems. It is a mixture density estimator around local models. To be specific, the input regions together with output regions are treated to be Gaussian serving as local models. These models are linked by a linear or non-linear function involving the mixture of densities of local models. The present work shows a connection between the CWM and generalized fuzzy model (GFM) thus paving the way for utilizing the concepts of probability theory in the fuzzy domain that has already emerged as a versatile tool for solving problems in uncertain dynamic systems.en
dc.format.extent654421 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoenen
dc.subjectcluster-weighted modelingen
dc.subjectgeneralized fuzzy modelen
dc.titleCluster-weighted modeling as a basis for fuzzy modelingen
dc.typeArticleen
Appears in Collections:Electrical Engineering

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