Exponential stability of impulsive neural networks with time-varying delays

Zai-Tang Huang, Qi-Gui Yang and Xiao-shu Luo

Chaos, Solitons & Fractals, 2008, vol. 35, issue 4, 770-780

Abstract: This paper considers the problems of global exponential stability for impulsive neural networks with time-varying delays, some new criteria ensuring globally exponential stability are obtained. The results obtained impose constraint conditions on the network parameters of neural system independent and are applicable to all continuous non-monotonic neuron activation functions. Compared with the previously reported results in the literature, our results obtained in this paper provide better one more set of criteria for determining the stability of neural networks with time-varying delays. Moreover, two illustrative examples will be given to demonstrate the effectiveness of our results.

Date: 2008
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077906005169
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:35:y:2008:i:4:p:770-780

DOI: 10.1016/j.chaos.2006.05.089

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().