A fuzzy neural network based extended Kalman filter

dc.citation.epage	339	en_US
dc.citation.issue	3	en_US
dc.citation.spage	333	en_US
dc.citation.volume	27	en_US
dc.citation.woscount	5
dc.contributor.author	Chao, CT	en_US
dc.contributor.author	Teng, CC	en_US
dc.contributor.department	交大名義發表	zh_TW
dc.contributor.department	電控工程研究所	zh_TW
dc.contributor.department	National Chiao Tung University	en_US
dc.contributor.department	Institute of Electrical and Control Engineering	en_US
dc.date.accessioned	2014-12-08T15:02:47Z
dc.date.available	2014-12-08T15:02:47Z
dc.date.issued	1996-03-01	en_US
dc.description.abstract	In this paper we construct a discrete extended Kalman filter by using fuzzy neural networks. The constructed filter makes it possible to estimate the states of a nonlinear dynamic system with unknown plant model. The unknown plant is identified by a fuzzy neural network that avoids the occurrence of divergence in state estimation. A computer simulation is presented to illustrate the performance and application of the proposed filter.	en_US
dc.identifier.issn	0020-7721	en_US
dc.identifier.journal	INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE	en_US
dc.identifier.uri	https://ir.lib.nycu.edu.tw/handle/11536/1414
dc.identifier.wosnumber	WOS:A1996UJ69700008
dc.language.iso	en_US	en_US
dc.title	A fuzzy neural network based extended Kalman filter	en_US
dc.type	Article	en_US

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