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

Yen Ping Chen, Jeen-Shing Wang

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

7 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2004 IEEE International Joint Conference on Neural Networks - Proceedings
Pages849-854
Number of pages6
Volume2
DOIs
Publication statusPublished - 2004
Event2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary
Duration: 2004 Jul 252004 Jul 29

Other

Other2004 IEEE International Joint Conference on Neural Networks - Proceedings
CountryHungary
CityBudapest
Period04-07-2504-07-29

All Science Journal Classification (ASJC) codes

  • Software

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