Genetic Programming for the Identification of Nonlinear Input-Output Models

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Janos Madar and Janos Abonyi and Ferenc Szeifert",
  title =        "Genetic Programming for the Identification of
                 Nonlinear Input-Output Models",
  journal =      "Industrial and Engineering Chemistry Research",
  year =         "2005",
  volume =       "44",
  number =       "9",
  pages =        "3178--3186",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  DOI =          "doi:10.1021/ie049626e",
  abstract =     "Linear-in-parameters models are quite widespread in
                 process engineering, e.g., nonlinear additive
                 autoregressive models, polynomial ARMA models, etc.
                 This paper proposes a new method for the structure
                 selection of these models. The method uses genetic
                 programming to generate nonlinear input-output models
                 of dynamical systems that are represented in a tree
                 structure. The main idea of the paper is to apply the
                 orthogonal least squares (OLS) algorithm to estimate
                 the contribution of the branches of the tree to the
                 accuracy of the model. This method results in more
                 robust and interpretable models. The proposed approach
                 has been implemented as a freely available MATLAB
                 Toolbox, The simulation
                 results show that the developed tool provides an
                 efficient and fast method for determining the order and
                 structure for nonlinear input-output models.",
  notes =        "

                 S0888-5885(04)09626-5 American Chemical Society
                 Department of Process Engineering, University of
                 Veszprem, P.O. Box 158, Veszprem 8201, Hungary",

Genetic Programming entries for Janos Madar Janos Abonyi Ferenc Szeifert