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ϵ-insensitive fuzzy c-regression models: introduction to ϵ-insensitive fuzzy modeling

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1 Author(s)
J. M. Leski ; Div. of Biomed. Electron., Silesian Univ. of Technol., Zabrze, Poland

This paper introduces a new ε-insensitive fuzzy c-regression models (εFCRM), that can be used in fuzzy modeling. To fit these regression models to real data, a weighted ε-insensitive loss function is used. The proposed method make it possible to exclude an intrinsic inconsistency of fuzzy modeling, where crisp loss function (usually quadratic) is used to match real data and the fuzzy model. The ε-insensitive fuzzy modeling is based on human thinking and learning. This method allows easy control of generalization ability and outliers robustness. This approach leads to c simultaneous quadratic programming problems with bound constraints and one linear equality constraint. To solve this problem, computationally efficient numerical method, called incremental learning, is proposed. Finally, examples are given to demonstrate the validity of introduced approach to fuzzy modeling.

Published in:

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)  (Volume:34 ,  Issue: 1 )