Robust Adaptive Identification of Fuzzy Systems with Uncertain Data

Authors:
Mohit Kumar;Regina Stoll;Norbert Stoll
Affiliations:
Institute of Occupational and Social Medicine, Faculty of Medicine, University of Rostock, D-18055 Rostock, Germany/ mohit.kumar@etechnik.uni-rostock.de;Institute of Occupational and Social Medicine, Faculty of Medicine, University of Rostock, D-18055 Rostock, Germany/ regina.stoll@med.uni-rostock.de;Institute of Automation, Department of Electrical Engineering and Information Technology, University of Rostock, D-18119 Rostock-Warnemnde, Germany/ norbert.stoll@etechnik.uni-rostock.de
Venue:
Fuzzy Optimization and Decision Making
Year:
2004

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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.