Conference Paper

# Cluster-weighted 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

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**ABSTRACT:**The Cluster-Weighted 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 non-additive fuzzy system versions of GFM and a case study1 is given -
##### Conference Paper: Cluster-Weighted Modeling as a Basis for Non-Additive GFM

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**ABSTRACT:**The cluster-weighted 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 non-additive fuzzy system versions of GFMFuzzy Systems, 2005. FUZZ '05. The 14th IEEE International Conference on; 06/2005 -
##### Conference Paper: Interactive Fuzzy System Using CWM

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**ABSTRACT:**The Cluster-Weighted 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

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