Identification of Fuzzy Models Using Cartesian Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

  author =       "S. Yazdani and M. {Aliyari shoorehdeli} and 
                 M. Teshnehlab",
  title =        "Identification of Fuzzy Models Using Cartesian Genetic
  booktitle =    "International Conference on Computational Intelligence
                 and Security, CIS '08",
  year =         "2008",
  month =        dec,
  volume =       "2",
  pages =        "76--81",
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 genetic programming, complex optimization problem,
                 fuzzy clustering, fuzzy models, membership function
                 parameters, pattern recognition, recursive least
                 square, combinatorial mathematics, fuzzy set theory,
                 fuzzy systems, least squares approximations, pattern
                 clustering, recursive estimation",
  DOI =          "doi:10.1109/CIS.2008.143",
  abstract =     "Fuzzy models have capability for solving problem in
                 different application such as pattern recognition,
                 prediction and control. Nevertheless, it has to be
                 emphasized that the identification of a fuzzy model is
                 complex task with many local minima. Cartesian genetic
                 programming provides a way to solve such complex
                 optimization problem. In this paper, fuzzy model is in
                 form of network. Cartesian genetic programming is used
                 to optimize the antecedent part and recursive least
                 square is used to optimized the consequent part. The
                 initialization of membership function parameters are
                 doing with fuzzy clustering. Benefit of the methodology
                 is illustrated by simulation results.",
  notes =        "Also known as \cite{4724740}",

Genetic Programming entries for Samaneh Yazdani Mahdi Aliyari shoorehdeli M Teshnehlab