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Fuzzy Modeling for Uncertainty Nonlinear Systems with Fuzzy Equations

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  • Raheleh Jafari
  • Wen Yu

Abstract

The uncertain nonlinear systems can be modeled with fuzzy equations by incorporating the fuzzy set theory. In this paper, the fuzzy equations are applied as the models for the uncertain nonlinear systems. The nonlinear modeling process is to find the coefficients of the fuzzy equations. We use the neural networks to approximate the coefficients of the fuzzy equations. The approximation theory for crisp models is extended into the fuzzy equation model. The upper bounds of the modeling errors are estimated. Numerical experiments along with comparisons demonstrate the excellent behavior of the proposed method.

Suggested Citation

  • Raheleh Jafari & Wen Yu, 2017. "Fuzzy Modeling for Uncertainty Nonlinear Systems with Fuzzy Equations," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-10, January.
    • Handle: RePEc:hin:jnlmpe:8594738
    DOI: 10.1155/2017/8594738
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    Cited by:

    1. Panigrahi, Paresh Kumar & Nayak, Sukanta, 2024. "Numerical approach to solve imprecisely defined systems using Inner Outer Direct Search optimization technique," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 215(C), pages 578-606.

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