Global exponential stabilization of delayed BAM neural networks: a matrix measure approach

DOI

10.2991/macmc-17.2018.56How to use a DOI?

Keywords

global exponential stabilization; BAM neural networks; time delays; matrix measure; Lyapunov stability theory

Abstract

In this paper, global exponential stabilization of bidirectional associative memory (BAM) neural networks with time delays is investigated. Based on the Lyapunov stability theory, we present several sufficient conditions for the global exponential stability of the equilibrium point of the BAM neural networks with delays via matrix measure approach. The presented results are easy to verify and simple to implement in practice. Finally, one numerical example is given to illustrate the feasibility and effectiveness of our theoretical results.

Copyright

© 2018, 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  - Yong Li
PY  - 2018/01
DA  - 2018/01
TI  - Global exponential stabilization of delayed BAM neural networks: a matrix measure approach
BT  - Proceedings of the 2017 4th International Conference on Machinery, Materials and Computer (MACMC 2017)
PB  - Atlantis Press
SP  - 260
EP  - 267
SN  - 2352-5401
UR  - https://doi.org/10.2991/macmc-17.2018.56
DO  - 10.2991/macmc-17.2018.56
ID  - Li2018/01
ER  -