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Monte Carlo methods in fuzzy linear regression II

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Abstract

We apply our new fuzzy Monte Carlo method to a certain fuzzy linear regression problem to estimate the best solution. The best solution is a vector of crisp numbers, for the coefficients in the model, which minimizes one of two error measures. We use a quasi-random number generator to produce random sequences of these crisp vectors which uniformly fill the search space. We consider an example problem and show this Monte Carlo method obtains the best solution for both error measures.

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Authors and Affiliations

  1. Department of Mathematics, University of Alabama at Birmingham, Birmingham, AL, 35294, USA

    Areeg Abdalla & James J. Buckley

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  1. Areeg Abdalla
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  2. James J. Buckley
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Correspondence to James J. Buckley.

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Abdalla, A., Buckley, J.J. Monte Carlo methods in fuzzy linear regression II. Soft Comput 12, 463–468 (2008). https://doi.org/10.1007/s00500-007-0179-6

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  • DOI: https://doi.org/10.1007/s00500-007-0179-6

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