Gradient algorithms with improved convergence [articol]

Partheniu, Cezar

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DC Field	Value	Language
dc.contributor.author	Partheniu, Cezar	-
dc.date.accessioned	2021-09-16T07:10:34Z	-
dc.date.available	2021-09-16T07:10:34Z	-
dc.date.issued	2004	-
dc.identifier.citation	Partheniu, Cezar. Gradient algorithms with improved convergence. Timişoara: Editura Politehnica, 2004	en_US
dc.identifier.uri	http://primo.upt.ro:1701/primo-explore/search?query=any,contains,Gradient%20algorithms%20with%20improved%20convergence&tab=default_tab&search_scope=40TUT&vid=40TUT_V1&lang=ro_RO&offset=0 Link Primo	-
dc.description.abstract	A generalized normalized gradient descend (GNGD) algorithm for linear finite-impulse response is presented and analized. The GNGD is an extension of the normalized least mean square (NLMS) algorithm by means of an additional gradient adaptive term in the denominator of the learning rate of NLMS. GNGD has better convergence in linear prediction configuration than other algorithms, good performances in system identification configuration in some conditions, worse response in interferences cancelling configuration and similar results with NLMS in reverse modelling configuration.	en_US
dc.language.iso	en	en_US
dc.publisher	Timişoara : Editura Politehnica	en_US
dc.relation.ispartofseries	Buletinul ştiinţific al Universităţii „Politehnica” din Timişoara, România. Seria electronică şi telecomunicaţii, Tom 49(63), fasc. 2 (2004), p. 75-80	-
dc.subject	Gradient adaptive learning rate	en_US
dc.subject	Adaptive filtering	en_US
dc.subject	Generalized normalized gradient descent	en_US
dc.title	Gradient algorithms with improved convergence [articol]	en_US
dc.type	Article	en_US
Appears in Collections:	Articole științifice/Scientific articles

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