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Robust Adaptive Fuzzy Identification of Time-Varying Processes with Uncertain Data. Handling Uncertainties in the Physical Fitness Fuzzy Approximation with Real World Medical Data: An Application

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Abstract

This study considers the problem of Robust Fuzzy approximation of a time-varying nonlinear process in the presence of uncertainties in the identification data using a Sugeno Fuzzy System while maintaining the interpretability of the fuzzy model during identification. A recursive procedure for the estimation of fuzzy parameters is proposed based on solving local optimization problem that attempt to minimize the worst-case effect of data uncertainties on approximation performance. To illustrate the approach, several simulation studies on numerical examples are provided. The developed scheme was applied to handle the vagueness, ambiguity and uncertainty inherently present in the general notion of a Medical Expert about Physical Fitness based on a set of various Physiological parameters measurements.

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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

Authors
  1. Mohit Kumar
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  2. Regina Stoll
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  3. Norbert Stoll
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Kumar, M., Stoll, R. & Stoll, N. Robust Adaptive Fuzzy Identification of Time-Varying Processes with Uncertain Data. Handling Uncertainties in the Physical Fitness Fuzzy Approximation with Real World Medical Data: An Application. Fuzzy Optimization and Decision Making 2, 243–259 (2003). https://doi.org/10.1023/A:1025046621254

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