Robust Stability Criteria for Uncertain Stochastic Cellular Neural Networks with Time Delays

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

Qiu, Jiqing, Gao, Zhifeng, Wang, Jufang and Shi, Peng (2007) Robust Stability Criteria for Uncertain Stochastic Cellular Neural Networks with Time Delays. In: Second International Conference on Innovative Computing, Information and Control, 2007. ICICIC '07. IEEE, New York, N.Y., pp. 556-559.

Abstract

In this paper, the global robust asymptotic stability problem is considered for stochastic cellular neural networks with time delays and parameter uncertainties. The aim of this paper is to establish easily verifiable conditions under which the stochastic cellular neural networks is globally robustly asymptotically stable in the mean square for all admissible parameter uncertainties. Base on Lyapunov- Krasovskii functional and stochastic analysis approaches, a linear matrix inequality (LMI) approach is developed to derive the stability criteria. A numerical example is provided to illustrate the effectiveness and applicability of the proposed criteria.

Dimensions Badge

Altmetric Badge

Item type Book Section
URI https://vuir.vu.edu.au/id/eprint/6376
DOI https://doi.org/10.1109/ICICIC.2007.503
Official URL http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arn...
ISBN 0769528821, 9780769528823
Subjects Historical > FOR Classification > 0906 Electrical and Electronic Engineering
Historical > Faculty/School/Research Centre/Department > Institute for Logistics and Supply Chain Management (ILSCM)
Historical > FOR Classification > 0199 Other Mathematical Sciences Information Systems
Keywords ResPubID18961, Lyapunov methods, asymptotic stability, cellular neural nets, time delays, linear matrix inequalities, stochastic systems, uncertain systems
Citations in Scopus 1 - View on Scopus
Download/View statistics View download statistics for this item

Search Google Scholar

Repository staff login