Conference Paper
Clusterweighted modeling as a basis for fuzzy modeling
Dept. of Electr. Eng., IIT, New Delhi, India
DOI: 10.1109/ITCC.2003.1197603 Conference: Information Technology: Coding and Computing [Computers and Communications], 2003. Proceedings. ITCC 2003. International Conference on Source: IEEE Xplore

Conference Paper: Interactive Fuzzy System Using CWM
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ABSTRACT: The ClusterWeighted Modeling (CWM) is a mixture density estimator around local models. The input regions together with output regions are treated to be Gaussian serving as local models. These models are linked by a linear function involving the mixture of densities of local models. A connection between the CWM and Generalized Fuzzy Model (GFM) is established in this work for utilizing the concepts of probability theory in deriving interactive fuzzy system version of GFM.INDICON, 2005 Annual IEEE; 01/2006 
Conference Paper: ClusterWeighted Modeling as a Basis for NonAdditive GFM
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ABSTRACT: The clusterweighted modeling (CWM) 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 function involving the mixture of densities of local models. A connection between the CWM and generalized fuzzy model (GFM) is established in this work for utilizing the concepts of probability theory in deriving additive and nonadditive fuzzy system versions of GFMFuzzy Systems, 2005. FUZZ '05. The 14th IEEE International Conference on; 06/2005  [Show abstract] [Hide abstract]
ABSTRACT: The ClusterWeighted Modeling (CWM) 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 function involving the mixture of densities of local models. A connection between the CWM and Generalized Fuzzy Model (GFM) is established in this work for utilizing the concepts of probability theory in deriving additive and nonadditive fuzzy system versions of GFM and a case study1 is given
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