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Fuzzy Optimization and Decision Making

, Volume 3, Issue 3, pp 195–216 | Cite as

Robust Adaptive Identification of Fuzzy Systems with Uncertain Data

  • Mohit Kumar
  • Regina Stoll
  • Norbert Stoll
Article

Abstract

This study presents a method of adaptive identification of parameters describing Sugeno fuzzy inference system in presence of bounded disturbances while maintaining the readability and interpretability of the fuzzy model during and after identification. This method do not require any a priori knowledge of a bound on the disturbance and noise and of a bound on the unknown parameters values. The method can be used for the robust and adaptive identification of slowly time varying nonlinear systems using fuzzy inference systems. The suggested method was used to build a fuzzy expert system that approximates the functional relationship between physical fitness and some of the measurable physiological parameters by their real measurements and opinion (human-experiences) of a medical expert.

fuzzy-modelling identification robustness nonlinear least squares 

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Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Mohit Kumar
    • 1
  • Regina Stoll
    • 2
  • Norbert Stoll
    • 3
  1. 1.University of RostockRostockGermany
  2. 2.Institute of Occupational and Social Medicine, Faculty of MedicineUniversity of RostockRostockGermany
  3. 3.Institute of Automation, Department of Electrical Engineering and Information TechnologyUniversity of RostockRostock-WarnemndeGermany

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