Linked Life Data

19244019

Source:http://linkedlifedata.com/resource/pubmed/id/19244019

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2009-4-3
pubmed:abstractText
This paper proposes a novel computational efficient adaptive nonlinear equalizer based on combination of finite impulse response (FIR) filter and functional link artificial neural network (CFFLANN) to compensate linear and nonlinear distortions in nonlinear communication channel. This convex nonlinear combination results in improving the speed while retaining the lower steady-state error. In addition, since the CFFLANN needs not the hidden layers, which exist in conventional neural-network-based equalizers, it exhibits a simpler structure than the traditional neural networks (NNs) and can require less computational burden during the training mode. Moreover, appropriate adaptation algorithm for the proposed equalizer is derived by the modified least mean square (MLMS). Results obtained from the simulations clearly show that the proposed equalizer using the MLMS algorithm can availably eliminate various intensity linear and nonlinear distortions, and be provided with better anti-jamming performance. Furthermore, comparisons of the mean squared error (MSE), the bit error rate (BER), and the effect of eigenvalue ratio (EVR) of input correlation matrix are presented.
pubmed:language
eng
pubmed:journal
pubmed:status
PubMed-not-MEDLINE
pubmed:month
Apr
pubmed:issn
1941-0093
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
20
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
665-74
pubmed:dateRevised
2009-10-28
pubmed:year
2009
pubmed:articleTitle
Adaptively combined FIR and functional link artificial neural network equalizer for nonlinear communication channel.
pubmed:affiliation
Si-Chuan province Key Lab of Signal and Information Processing, Southwest Jiaotong University, Chengdu, China. hqzhao0815@yahoo.com.cn
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't