A novel recurrent neural network with minimal representation for dynamic system identification

Yen Ping Chen, Jeen-Shing Wang

研究成果: Conference contribution

7 引文 斯高帕斯(Scopus)

摘要

This paper presents a self-adaptive learning algorithm for dynamic system identification using a novel recurrent neural network with minimal representation. The proposed algorithm consists of two mechanisms, a minimal realization technique based on Markov parameters and a recursive parameter learning method on the ordered derivatives, for the minimal order identification and parameter optimization, respectively. Computer simulations on unknown dynamic system identification using the proposed approach have successfully validated: 1) the order of the recurrent network representation is minimal, and 2) the proposed network is able to closely capture the dynamical behavior of the unknown system with a satisfactory performance.

原文English
主出版物標題2004 IEEE International Joint Conference on Neural Networks - Proceedings
頁面849-854
頁數6
2
DOIs
出版狀態Published - 2004
事件2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary
持續時間: 2004 七月 252004 七月 29

Other

Other2004 IEEE International Joint Conference on Neural Networks - Proceedings
國家Hungary
城市Budapest
期間04-07-2504-07-29

All Science Journal Classification (ASJC) codes

  • Software

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