Characterization and detection of noise in clustering
Pattern Recognition Letters
A course in fuzzy systems and control
A course in fuzzy systems and control
Fuzzy Modeling for Control
Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Development of a systematic methodology of fuzzy logic modeling
IEEE Transactions on Fuzzy Systems
On the interpretation and identification of dynamic Takagi-Sugeno fuzzy models
IEEE Transactions on Fuzzy Systems
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
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In this paper, application of possibilistic clustering techniques to identification of local linear models will be discussed. In particular, a generalisation of some possibilistic algorithms in the bibliography is obtained. With the presented procedures, a trade-off between an ''expected shape'' of the membership functions and model fit can be stated. Possibilistic clustering may allow for better detection of undermodelling and overmodelling than basic techniques based on fuzzy partitions. ions.