EconPapers    
Economics at your fingertips  
 

Exponential stability of reaction–diffusion generalized Cohen–Grossberg neural networks with time-varying delays

Qinghua Zhou, Li Wan and Jianhua Sun

Chaos, Solitons & Fractals, 2007, vol. 32, issue 5, 1713-1719

Abstract: The stability property of reaction–diffusion generalized Cohen–Grossberg neural networks (GDCGNNs) with time-varying delay are considered. Without assuming the monotonicity and differentiability of activation functions, nor symmetry of synaptic interconnection weights, delay independent and easily verifiable sufficient conditions to guarantee the exponential stability of an equilibrium solution associated with temporally uniform external inputs to the networks are obtained, by employing the method of variational parameter and inequality technique. One example is given to illustrate the theoretical results.

Date: 2007
References: View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077905012105
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:32:y:2007:i:5:p:1713-1719

DOI: 10.1016/j.chaos.2005.12.003

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. ().

 
Page updated 2025-03-19
Handle: RePEc:eee:chsofr:v:32:y:2007:i:5:p:1713-1719