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dc.contributor.authorXiao, S.
dc.contributor.authorLian, H.
dc.contributor.authorTeo, Kok Lay
dc.contributor.authorZeng, H.
dc.contributor.authorZhang, X.
dc.date.accessioned2018-12-13T09:12:22Z
dc.date.available2018-12-13T09:12:22Z
dc.date.created2018-12-12T02:46:42Z
dc.date.issued2018
dc.identifier.citationXiao, S. and Lian, H. and Teo, K.L. and Zeng, H. and Zhang, X. 2018. A new Lyapunov functional approach to sampled-data synchronization control for delayed neural networks. Journal of the Franklin Institute. 355 (17): pp. 8857-8873.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/72102
dc.identifier.doi10.1016/j.jfranklin.2018.09.022
dc.description.abstract

This paper discusses the problem of synchronization for delayed neural networks using sampled-data control. We introduce a new Lyapunov functional, called complete sampling-interval-dependent discontinuous Lyapunov functional, which can adequately capture sampling information on both intervals from r(t-t¯) to r(tk-t¯) and from r(t-t¯) to r(tk+1-t¯). Based on this Lyapunov functional and an improved integral inequality, less conservative conditions are derived to ensure the stability of the synchronization error system, leading to the fact that the drive neural network is synchronized with the response neural network. The desired sampled-data controller is designed in terms of solutions to linear matrix inequalities. A numerical example is provided to demonstrate that the proposed approaches are effective and superior to some existing ones in the literature.

dc.publisherElsevier
dc.titleA new Lyapunov functional approach to sampled-data synchronization control for delayed neural networks
dc.typeJournal Article
dcterms.source.volume355
dcterms.source.number17
dcterms.source.startPage8857
dcterms.source.endPage8873
dcterms.source.issn0016-0032
dcterms.source.titleJournal of the Franklin Institute
curtin.departmentSchool of Electrical Engineering, Computing and Mathematical Science (EECMS)
curtin.accessStatusFulltext not available


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