Soft Computing

, Volume 6, Issue 6, pp 470–475

Genetic algorithms-based fuzzy regression analysis

  • R. A. Aliev
  • B. Fazlollahi
  • R. Vahidov
Original paper

DOI: 10.1007/s00500-002-0163-0

Cite this article as:
Aliev, R., Fazlollahi, B. & Vahidov, R. Soft Computing (2002) 6: 470. doi:10.1007/s00500-002-0163-0

Abstract

 This paper describes the concept of fuzzy regression analysis based on genetic algorithms. It is shown that the performance of fuzzy regression models may be improved and fuzzy modeling technique can be simplified by incorporating genetic algorithms into regression analysis procedure. The effectiveness of the proposed approach is illustrated through simulation of fuzzy linear regression model obtained by other authors and comparison of the results. The paper further demonstrates the applications of the approach to the manufacturing and business problems.

Keywords Fuzzy set, Fuzzy regression, Genetic algorithms, Fuzzy distance

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • R. A. Aliev
    • 1
  • B. Fazlollahi
    • 2
  • R. Vahidov
    • 3
  1. 1.Department of Automatic Control Systems, Azerbaijan State Oil Academy, 20 Azadlyg Prospekt, Baku, AzerbaijanAZ
  2. 2.Institute of International Business, Georgia State University, 35 Broad Street, Atlanta, GA 30303, USA E-mail: bijan@gsu.eduUS
  3. 3.Department of Decision Sciences & MIS, John Molson School of Business, Concordia University, 1455 de Maisonneuve Blvd. W., Montreal, Quebec, CanadaCA