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Monte Carlo Methods In Fuzzy Non-Linear Regression

Author

Listed:
  • AREEG ABDALLA

    (Mathematics Department, University of Alabama at Birmingham, Birmingham, Alabama, 35294, USA)

  • JAMES BUCKLEY

    (Mathematics Department, University of Alabama at Birmingham, Birmingham, Alabama, 35294, USA)

Abstract

We apply our new fuzzy Monte Carlo method to certain fuzzy non-linear regression problems to estimate the best solution. The best solution is a vector of triangular fuzzy numbers, for the fuzzy coefficients in the model, which minimizes an error measure. We use a quasi-random number generator to produce random sequences of these fuzzy vectors which uniformly fill the search space. We consider example problems to show that this Monte Carlo method obtains solutions comparable to those obtained by an evolutionary algorithm.

Suggested Citation

  • Areeg Abdalla & James Buckley, 2008. "Monte Carlo Methods In Fuzzy Non-Linear Regression," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 4(02), pages 123-141.
    • Handle: RePEc:wsi:nmncxx:v:04:y:2008:i:02:n:s1793005708000982
    DOI: 10.1142/S1793005708000982
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