Improved Stability Criteria of Generalized Recurrent Neural Networks with Time-varying Delays

DOI

10.2991/iccnce.2013.143How to use a DOI?

Keywords

Recurrent neural networks, Global asymptotically stability, Time-varying delays, Linear matrix inequality

Abstract

In this paper, the problem on stability analysis of generalized recurrent neural networks with a time-varying delays is considered. Neither the differentiability, the monotony on these activation functions nor the differentiability on the time-varying delays are assumed. By employing a new Lyapunov-Krasovskii function, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions for RNNs to be globally asymptotically stable. The proposed stability results are less conservative than some recently known ones in the literature. Finally an example is given to verify the effectiveness of the present criterion.

Copyright

© 2013, 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  - Jinfang Zhang
AU  - Yuanhua Qiao
AU  - Jun Miao
AU  - Lijuan Duan
PY  - 2013/07
DA  - 2013/07
TI  - Improved Stability Criteria of Generalized Recurrent Neural Networks with Time-varying Delays
BT  - Proceedings of the International Conference on Computer, Networks and Communication Engineering (ICCNCE 2013)
PB  - Atlantis Press
SP  - 577
EP  - 580
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccnce.2013.143
DO  - 10.2991/iccnce.2013.143
ID  - Zhang2013/07
ER  -