Linked Life Data

11227082

Source:http://linkedlifedata.com/resource/pubmed/id/11227082

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
10
pubmed:dateCreated
2001-1-11
pubmed:abstractText
We utilize the Lyapunov function method to analyze stability of continuous nonlinear neural networks with delays and obtain some new sufficient conditions ensuring the globally asymptotic stability independent of delays. Three main conditions imposed on the weighting matrices are established. (i). The spectral radius rho(M(-1)(W0 + Wtau)K) < 1. (ii). The row norm M(-1)(W0 + Wtau)K + P(-1) ((W0 + Wtau)KM(-1))T P infinity < 2. (iii). mu2(W0) + Wtau2,F < (m/k). These three conditions are independent to each other. The delayed Hopfield network, Bidirectional associative memory network and cellular neural network are special cases of the network model considered in this paper. So we improve some previous works of other researchers.
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
0893-6080
pubmed:author
pubmed:issnType
Print
pubmed:volume
13
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1135-43
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2000
pubmed:articleTitle
On stability of nonlinear continuous-time neural networks with delays.
pubmed:affiliation
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China. htlu@mail1.sjtu.edu.cn
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't