Global robust stability of interval neural networks with multiple time-varying delays

Qiankun Song and Jinde Cao

Mathematics and Computers in Simulation (MATCOM), 2007, vol. 74, issue 1, 38-46

Abstract: In this paper, the global robust stability is investigated for interval neural networks with multiple time-varying delays. The neural network contains time-invariant uncertain parameters whose values are unknown but bounded in given compact sets. Without assuming both the boundedness on the activation functions and the differentiability on the time-varying delays, a new sufficient condition is presented to ensure the existence, uniqueness, and global robust stability of equilibria for interval neural networks with multiple time-varying delays based on the Lyapunov–Razumikhin technique as well as matrix inequality analysis. Several previous results are improved and generalized, and an example is given to show the effectiveness of the obtained results.

Keywords: Global robust stability; Interval neural networks; Multiple time-varying delays; Lyapunov–Razumikhin technique (search for similar items in EconPapers)
Date: 2007
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S037847540600214X
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:matcom:v:74:y:2007:i:1:p:38-46

DOI: 10.1016/j.matcom.2006.06.030

Access Statistics for this article

Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens

More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier
Bibliographic data for series maintained by Catherine Liu ().