- 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",
