Fuzzy Optimization and Decision Making

, Volume 2, Issue 4, pp 317–336 | Cite as

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

  • Mohit Kumar
  • Regina Stoll
  • Norbert Stoll


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.

fuzzy approximation model identification ill-posed regularization nonlinear least squares inverse control generalized predictive control 


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Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • Mohit Kumar
    • 1
  • Regina Stoll
    • 1
  • Norbert Stoll
    • 2
  1. 1.Institute of Occupational and Social Medicine, Faculty of MedicineUniversity of RostockRostockGermany
  2. 2.Institute of Automation, Department of Electrical Engineering and Information TechnologyUniversity of RostockRostock-WarnemndeGermany

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