New stability criteria for Cohen–Grossberg neural networks with time delays

Full text for this resource is not available from the Research Repository.

Hu, Liang, Gao, H and Shi, Peng (2009) New stability criteria for Cohen–Grossberg neural networks with time delays. IET Control Theory and Applications, 3 (9). pp. 1275-1282. ISSN 1751-8644

Abstract

The asymptotic stability is investigated for a class of time-delay Cohen-Grossberg neural networks, either with or without parameter uncertainties. By introducing a novel Lyapunov functional with the ideal of delay fractioning, a new criterion of asymptotic stability is derived in terms of a linear matrix inequality (LMI), which can be efficiently solved via standard numerical software. The criterion proves to be less conservative and the conservatism could be notably reduced by thinning the delay fractioning. Two examples are provided to demonstrate the less conservatism and effectiveness of the proposed stability conditions.

Dimensions Badge

Altmetric Badge

Item type Article
URI https://vuir.vu.edu.au/id/eprint/4350
DOI https://doi.org/10.1049/iet-cta.2008.0213
Official URL http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arn...
Subjects Historical > Faculty/School/Research Centre/Department > Institute for Logistics and Supply Chain Management (ILSCM)
Historical > FOR Classification > 0906 Electrical and Electronic Engineering
Historical > SEO Classification > 970109 Expanding Knowledge in Engineering
Keywords ResPubID17723, asymptotic stability, Cohen-Grossberg neural network, time delay, parameter uncertain system, Lyapunov function, linear matrix inequality
Citations in Scopus 33 - View on Scopus
Download/View statistics View download statistics for this item

Search Google Scholar

Repository staff login