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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AI-Naffouri, T. Y. and A. H. Sayed. (2000). “An Adaptive Filter Robust to Data Uncertainties,” In Proc. Allerton. Conference on Communication, Control and Computing. Allerton, IL, 1175–1183 (October).
Babuska, R. (2000). “Construction of Fuzzy Systems-Interplay between Precision and Transparency,” Proc. ESIT 2000. Aachen, 445–452.
Bodenhofer, U. and P. Bauer. (2000). “Towards an Axiomatic Treatment of Interpretability,” In Proc. IIZUKA2000. Iizuka, 334–339 (October).
Burger, M., H. W. Engl, J. Haslinger, and U. Bodenhofer. (2002). “Regularized Data-Driven Construction of Fuzzy Controllers,” J. Inverse and Ill-posed Problems 10(2002), 319–344.
Espinosa, J. and J. Vandewalle. (2000). “Constructing Fuzzy Models with Linguistic Integrity from Numerical Data-AFRELI Algorithm,” IEEE Trans. Fuzzy Systems 8(5), 591–600 (October).
Jang, J.-S. Roger. (1993). “ANFIS: Adaptive-Network-Based Fuzzy Inference Systems,” IEEE Trans. Syst. Man Cybern 23(3), 665–685.
Lawson, C. L. and R. J. Hanson. (1995). Solving Least Squares Problems. Philadelphia: SIAM Publications.
Nauck, D. and R. Kruse. (1997). “Function Approximation by NEFPROX,” Proc. Second European Workshop on Fuzzy Decision Analysis and Neural Networks for Management, Planning, and Optimization (EFDAN'97). Dortmund, 160–169.
Nauck, D. and R. Kruse. (1998). “A Neuro-Fuzzy Approach to Obtain Interpretable Fuzzy Systems for Function Approximation,” Proc. IEEE International Conference on Fuzzy Systems 1998 (FUZZ-IEEE'98). AK: Anchorage, 1106–1111 (May 4–9).
Nauck, D. and R. Kruse. (1999). “Obtaining Interpretable Fuzzy Classiffication Rules from Medical Data,” Artificial Intelligence in Medicine 16, 149–169.
Setnes, M., R. Babuka, and H. B. Verbruggen. (1998). “Rule-Based Modeling: Precision and Transparency,” IEEE Trans. Syst. Man Cybern.Part C: Applications and Reviews 28, 165–169.
Takagi, T. and M. Sugeno. (1985). “Fuzzy Identification of Systems and Its Applications to Modeling and Control,” IEEE Trans. Syst. Man Cybern 15(1), 116–132.
Väinämö, K., S. Nissilä, T. Mäkikallio, M. Tulppo, and J. Röning. (1996). “Artificial Neural Network for Aerobic Fitness Approximation,” International Conference on Neural Networks (ICNN96). Washington DC, USA (June 3–6).
Väinämö, K., T. Mäkikallio, M. Tulppo, and J. Röning. (1998). “A Neuro-Fuzzy Approach to Aerobic Fitness Classiffication: A Multistructure Solution to the Context-Sensitive Feature Selection Problem,” Proc. WCCI' 98. Alaska, USA: Anchorage, 797–802 (May 4–9).
Widrow, B. and M. A. Lehr. (1990). “30 Years of Adaptive Neural Networks: Perceptron, Madline and Backpropagation,” Proceeding of the IEEE 78(9), 1415–1422.
Zadeh, L. A. (1973). “Outline of a New Approach to the Analysis of Complex Systems and Decision Processes,” IEEE Trans. Syst. Man Cybern 3(1), 28–44.
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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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DOI: https://doi.org/10.1023/A:1025046621254