MONTE CARLO METHODS IN FUZZY NON-LINEAR REGRESSION

Areeg Abdalla () and James Buckley ()
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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

New Mathematics and Natural Computation (NMNC), 2008, vol. 04, issue 02, 123-141

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.

Keywords: Fuzzy non-linear regression; Monte Carlo; random fuzzy vectors (search for similar items in EconPapers)
Date: 2008
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DOI: 10.1142/S1793005708000982

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