Proceedings of the 2015 International Conference on Modeling, Simulation and Applied Mathematics

An Improved Delay-Dependent Globally Asymptotically Stability Criterion for the Static Neural Networks with Time-Varying Delay

Authors
Kai Mao, Bao Shi, Shudong Zhang
Corresponding Author
Kai Mao
Available Online August 2015.
DOI
https://doi.org/10.2991/msam-15.2015.33How to use a DOI?
Keywords
static neural networks; lyapunov-krasovskii functional; delay fractioning method; convex combination method; LMIs
Abstract
The globally asymptotic stability for static neural networks with time-varying delay is concerned in this paper. By delay fractioning technique and taking more delayed-state variables into account, a newly Lyapunov-Krasovskii Functional was constructed, together with the Jessen integral inequality and convex combination method , a delay-dependent global stability criterion is obtained, it is less conservative than some existing ones. Example is provided to show the effectiveness and reduced conservatism of the proposed results.
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Proceedings
2015 International Conference on Modeling, Simulation and Applied Mathematics
Part of series
Advances in Intelligent Systems Research
Publication Date
August 2015
ISBN
978-94-6252-104-9
ISSN
1951-6851
DOI
https://doi.org/10.2991/msam-15.2015.33How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Kai Mao
AU  - Bao Shi
AU  - Shudong Zhang
PY  - 2015/08
DA  - 2015/08
TI  - An Improved Delay-Dependent Globally Asymptotically Stability Criterion for the Static Neural Networks with Time-Varying Delay
BT  - 2015 International Conference on Modeling, Simulation and Applied Mathematics
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/msam-15.2015.33
DO  - https://doi.org/10.2991/msam-15.2015.33
ID  - Mao2015/08
ER  -