EconPapers    
Economics at your fingertips  
 

Exponential stability criterion for interval neural networks with discrete and distributed delays

Hao Chen, Shouming Zhong and Jinliang Shao

Applied Mathematics and Computation, 2015, vol. 250, issue C, 121-130

Abstract: This paper investigates the global exponential stability of neural networks with discrete and distributed delays. A new criterion for the exponential stability of neural networks with mixed delays is derived by using the Lyapunov stability theory, Homomorphic mapping theory and matrix theory. The obtained result is easier to be verified than those previously reported stability results. Finally, some illustrative numerical examples are given to show the effectiveness of the proposed result.

Keywords: Neural networks; Lyapunov functional; Distributed delay; Exponential stability; M-matrix theory (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0096300314014647
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:apmaco:v:250:y:2015:i:c:p:121-130

DOI: 10.1016/j.amc.2014.10.089

Access Statistics for this article

Applied Mathematics and Computation is currently edited by Theodore Simos

More articles in Applied Mathematics and Computation from Elsevier
Bibliographic data for series maintained by Catherine Liu ().

 
Page updated 2025-03-19
Handle: RePEc:eee:apmaco:v:250:y:2015:i:c:p:121-130