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

DOI

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.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - Proceedings of the 2015 International Conference on Modeling, Simulation and Applied Mathematics
PB  - Atlantis Press
SP  - 144
EP  - 150
SN  - 1951-6851
UR  - https://doi.org/10.2991/msam-15.2015.33
DO  - 10.2991/msam-15.2015.33
ID  - Mao2015/08
ER  -