Hammerstein Model Identification Method Based on Genetic Programming

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@InProceedings{hatanaka:2001:hmimbgp,
  author =       "Toshiharu Hatanaka and Katsuji Uosaki",
  title =        "Hammerstein Model Identification Method Based on
                 Genetic Programming",
  booktitle =    "Proceedings of the 2001 Congress on Evolutionary
                 Computation CEC2001",
  year =         "2001",
  pages =        "1430--1435",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, System
                 identification, Hammerstein models, Nonlinear systems,
                 Evolutionary computation, Akaike information criterion,
                 Hammerstein model identification method, genetic
                 programming, least square method, nonlinear dynamic
                 system, nonlinear static block, system identification,
                 training data, genetic algorithms, identification,
                 nonlinear dynamical systems",
  ISBN =         "0-7803-6658-1",
  DOI =          "doi:10.1109/CEC.2001.934359",
  abstract =     "We address a novel approach to identify a nonlinear
                 dynamic system for a Hammerstein model. The Hammerstein
                 model is composed of a nonlinear static block in series
                 with a linear, dynamic system block. The aim of system
                 identification is to provide the optimal mathematical
                 model of both nonlinear static and linear dynamic
                 system blocks in some appropriate sense. We use genetic
                 programming to determine the functional structure for
                 the nonlinear static block. Each individual in genetic
                 programming represents a nonlinear function structure.
                 The unknown parameters of the linear dynamic block and
                 the nonlinear static block given by each individual are
                 estimated with a least square method. The fitness is
                 evaluated by AIC (Akaike information criterion) as
                 representing the balance of model complexity and
                 accuracy. It is calculated with the number of nodes in
                 the genetic programming tree, the order of the linear
                 dynamic model and the accuracy of model for the
                 training data. The results of numerical studies
                 indicate the usefulness of proposed approach to
                 Hammerstein model identification",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number = .

                 AIC Akaike information criterion",
}

Genetic Programming entries for Toshiharu Hatanaka Katsuji Uosaki

Citations