Linked Life Data

19028076

Source:http://linkedlifedata.com/resource/pubmed/id/19028076

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2009-1-23
pubmed:abstractText
This paper is concerned with the stability analysis problem for a new class of discrete-time recurrent neural networks with mixed time-delays. The mixed time-delays that consist of both the discrete and distributed time-delays are addressed, for the first time, when analyzing the asymptotic stability for discrete-time neural networks. The activation functions are not required to be differentiable or strictly monotonic. The existence of the equilibrium point is first proved under mild conditions. By constructing a new Lyapnuov-Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions for the discrete-time neural networks to be globally asymptotically stable. As an extension, we further consider the stability analysis problem for the same class of neural networks but with state-dependent stochastic disturbances. All the conditions obtained are expressed in terms of LMIs whose feasibility can be easily checked by using the numerically efficient Matlab LMI Toolbox. A simulation example is presented to show the usefulness of the derived LMI-based stability condition.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
0893-6080
pubmed:author
pubmed:issnType
Print
pubmed:volume
22
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
67-74
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Asymptotic stability for neural networks with mixed time-delays: the discrete-time case.
pubmed:affiliation
Department of Mathematics, Yangzhou University, Yangzhou 225002, PR China. liuyurong@gmail.com
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't