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A novel approach to robust parameter estimation using neurofuzzy systems

Author

Listed:
  • da Silva, Ivan N.
  • de Arruda, Lucia V.R.
  • do Amaral, Wagner C.

Abstract

A novel approach for solving robust parameter estimation problems is presented for processes with unknown-but-bounded errors and uncertainties. An artificial neural network is developed to calculate a membership set for model parameters. Techniques of fuzzy logic control lead the network to its equilibrium points. Simulated examples are presented as an illustration of the proposed technique. The result represent a significant improvement over previously proposed methods.

Suggested Citation

  • da Silva, Ivan N. & de Arruda, Lucia V.R. & do Amaral, Wagner C., 1999. "A novel approach to robust parameter estimation using neurofuzzy systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 48(3), pages 251-268.
    • Handle: RePEc:eee:matcom:v:48:y:1999:i:3:p:251-268
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    References listed on IDEAS

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    1. Clement, Thierry & Gentil, Sylviane, 1988. "Reformulation of parameter identification with unknown-but-bounded errors," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 30(3), pages 257-270.
    2. Walter, Eric & Piet-Lahanier, Hélène, 1990. "Estimation of parameter bounds from bounded-error data: a survey," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 32(5), pages 449-468.
    3. Mo, S.H. & Norton, J.P., 1990. "Fast and robust algorithm to compute exact polytope parameter bounds," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 32(5), pages 481-493.
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    Cited by:

    1. Firat, Mahmut & Güngör, Mahmud, 2007. "River flow estimation using adaptive neuro fuzzy inference system," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 75(3), pages 87-96.

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