Lyapunov learning algorithm for Quasi-ARX neural network to identification of nonlinear dynamical system

Mohammad Abu Jami'in*, Imam Sutrisno, Jinglu Hu

*この研究の対応する著者

研究成果: Conference contribution

10 被引用数 (Scopus)

抄録

In this note, we present the modeling of nonlinear dynamical systems with Quasi-ARX neural network using Lyapunov algorithm in learning process. This work exploits the idea on learning algorithm in nonlinear kernel part of Quasi-ARX model to improve stability and fast convergence of error. The proposed algorithm is then employed to model and predict a classical nonlinear system with input dead zone and nonlinear dynamic systems, exhibiting the effectiveness of proposed algorithm. Based on the result of simulation, the proposed algorithm can make the error in process learning become fast convergence, ultimately bounded, and the error distributed uniformly.

本文言語English
ホスト出版物のタイトルProceedings 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
ページ3147-3152
ページ数6
DOI
出版ステータスPublished - 2012 12月 1
イベント2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012 - Seoul, Korea, Republic of
継続期間: 2012 10月 142012 10月 17

出版物シリーズ

名前Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN(印刷版)1062-922X

Other

Other2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
国/地域Korea, Republic of
CitySeoul
Period12/10/1412/10/17

ASJC Scopus subject areas

  • 電子工学および電気工学
  • 制御およびシステム工学
  • 人間とコンピュータの相互作用

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