A neural network approach to improving identification of nonlinear polynomial models

Takayuki Furuzuki, Yinshi Li, Kotaro Hirasawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

This paper presents a neural network approach to improving the identification of nonlinear polynomial model. The idea is to realize the identification in two steps. In the first step, a quasi-ARX neural network is first used to approximate the system under study; then a reasonable number of important monomial terms are selected as candidates, by introducing an importance index based on a Taylor expansion of the identified quasi-ARX neural network; In the second step, Genetic algorithm (GA) is applied to the selected important terms to further determine a set of significant terms to include in the polynomial model. In this way, the whole identification algorithm is implemented very efficiently. Numerical simulations are carried out to demonstrate the effectiveness of the proposed two-step identification method.

Original languageEnglish
Title of host publicationProceedings of the SICE Annual Conference
Pages1662-1667
Number of pages6
Publication statusPublished - 2005
EventSICE Annual Conference 2005 - Okayama
Duration: 2005 Aug 82005 Aug 10

Other

OtherSICE Annual Conference 2005
CityOkayama
Period05/8/805/8/10

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Keywords

  • Genetic algorithm
  • Identification
  • Neural network
  • Nonlinear polynomial model
  • Poly-nomial expansion

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Furuzuki, T., Li, Y., & Hirasawa, K. (2005). A neural network approach to improving identification of nonlinear polynomial models. In Proceedings of the SICE Annual Conference (pp. 1662-1667)