A Dynamic Neural Network Model for Nonlinear System Identification

dc.citation.epage	441	en_US
dc.citation.spage	440	en_US
dc.contributor.author	Wang, Chi-Hsu	en_US
dc.contributor.author	Chen, Pin-Cheng	en_US
dc.contributor.author	Lin, Ping-Zong	en_US
dc.contributor.author	Lee, Tsu-Tian	en_US
dc.contributor.department	電控工程研究所	zh_TW
dc.contributor.department	Institute of Electrical and Control Engineering	en_US
dc.date.accessioned	2014-12-08T15:02:33Z
dc.date.available	2014-12-08T15:02:33Z
dc.date.issued	2008	en_US
dc.description.abstract	In this paper, a new dynamic neural network based on the Hopfield neural network is proposed to perform the nonlinear system identification. Convergent analysis is performed by the Lyapunov-like criterion to guarantee the error convergence during identification. Simulation results demonstrate that the proposed dynamic neural network trained by the Lyapunov approach can obtain good identifted performance.	en_US
dc.identifier.isbn	978-1-4244-4115-0	en_US
dc.identifier.journal	PROCEEDINGS OF THE 2009 IEEE INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION	en_US
dc.identifier.uri	https://ir.lib.nycu.edu.tw/handle/11536/1208
dc.identifier.wosnumber	WOS:000274330000084
dc.language.iso	en_US	en_US
dc.subject	system identification	en_US
dc.subject	dynamic neural network	en_US
dc.subject	Hopfield neural network	en_US
dc.subject	Lyapunov criterion	en_US
dc.title	A Dynamic Neural Network Model for Nonlinear System Identification	en_US
dc.type	Proceedings Paper	en_US

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