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Regularized Adaptation of Fuzzy Inference Systems. Modelling the Opinion of a Medical Expert about Physical Fitness: An Application

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Abstract

This study presents a new approach to adaptation of Sugeno type fuzzy inference systems using regularization, since regularization improves the robustness of standard parameter estimation algorithms leading to stable fuzzy approximation. The proposed method can be used for modelling, identification and control of physical processes. A recursive method for on-line identification of fuzzy parameters employing Tikhonov regularization is suggested. The power of approach was shown by applying it to the modelling, identification, and adaptive control problems of dynamic processes. The proposed approach was used for modelling of human-decisions (experience) with a fuzzy inference system and for the fuzzy approximation of physical fitness with real world medical data.

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Authors and Affiliations

  1. Institute of Occupational and Social Medicine, Faculty of Medicine, University of Rostock, D-18055, Rostock, Germany

    Mohit Kumar & Regina Stoll

  2. Institute of Automation, Department of Electrical Engineering and Information Technology, University of Rostock, D-18119, Rostock-Warnemnde, Germany

    Norbert Stoll

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  1. Mohit Kumar
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  2. Regina Stoll
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Kumar, M., Stoll, R. & Stoll, N. Regularized Adaptation of Fuzzy Inference Systems. Modelling the Opinion of a Medical Expert about Physical Fitness: An Application. Fuzzy Optimization and Decision Making 2, 317–336 (2003). https://doi.org/10.1023/B:FODM.0000003952.07461.ed

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  • DOI: https://doi.org/10.1023/B:FODM.0000003952.07461.ed

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