DSpace Repository

Gradient algorithms with improved convergence [articol]

Show simple item record

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


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse

My Account