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

ABSTRACT Cluster-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.

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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.
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  • [Show abstract] [Hide abstract]
    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
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  • [Show abstract] [Hide abstract]
    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

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