Soft Computing

, Volume 12, Issue 5, pp 463–468

Monte Carlo methods in fuzzy linear regression II

Original Paper

DOI: 10.1007/s00500-007-0179-6

Cite this article as:
Abdalla, A. & Buckley, J.J. Soft Comput (2008) 12: 463. doi:10.1007/s00500-007-0179-6

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.

Keywords

Fuzzy linear regression Monte Carlo Random vectors 

Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  1. 1.Department of MathematicsUniversity of Alabama at BirminghamBirminghamUSA

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