A neural network approach to improving identification of nonlinear polynomial models

Jinglu Hu*, Yinshi Li, Kotaro Hirasawa

*この研究の対応する著者

研究成果: Paper査読

2 被引用数 (Scopus)

抄録

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.

本文言語English
ページ1662-1667
ページ数6
出版ステータスPublished - 2005 12 1
イベントSICE Annual Conference 2005 - Okayama, Japan
継続期間: 2005 8 82005 8 10

Conference

ConferenceSICE Annual Conference 2005
国/地域Japan
CityOkayama
Period05/8/805/8/10

ASJC Scopus subject areas

  • 制御およびシステム工学
  • コンピュータ サイエンスの応用
  • 電子工学および電気工学

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