A new approach to the identification of a fuzzy model

DOI10.1016/S0165-0114(97)00214-5zbMath0946.93011OpenAlexW1973246139MaRDI QIDQ1304226

Euntai Kim, Minkee Park, Seunghwan Ji, Mignon Park

Publication date: 22 September 1999

Published in: Fuzzy Sets and Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0165-0114(97)00214-5

zbMATH Keywords

Hough transformgradient descent algorithmclustering methodidentification of fuzzy models

Mathematics Subject Classification ID

System identification (93B30) Control/observation systems with incomplete information (93C41) Structure of families (Picard-Lefschetz, monodromy, etc.) (14D05)

Related Items (4)

Semiglobal stabilization of nonlinear systems using fuzzy control and singular perturbation methods ⋮ Modelling and simulation with neural and fuzzy‐neural networks of switched circuits ⋮ Takagi-Sugeno fuzzy model development of batch biotechnological processes ⋮ Fuzzy Sets and Systems. Author index volume 129 (2002)

Cites Work

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• Linear fuzzy regression
• Structure identification of fuzzy model
• Generalizing the Hough transform to detect arbitrary shapes
• Successive identification of a fuzzy model and its applications to prediction of a complex system
• Fuzzy identification of systems and its applications to modeling and control
• Detection and Characterization of Cluster Substructure I. Linear Structure: Fuzzy c-Lines
• Detection and Characterization of Cluster Substructure II. Fuzzy c-Varieties and Convex Combinations Thereof

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