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Neural Computing and Applications
Published in: Neural Computing and Applications 1/2013

01-01-2013 | Cont. Dev. of Neural Compt. & Appln.

New conditions for global exponential stability of continuous-time neural networks with delays

Authors: Haibo Gao, Xingguo Song, Liang Ding, Deyou Liu, Minghui Hao

Published in: Neural Computing and Applications | Issue 1/2013

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Abstract

In this paper, we investigate the global exponential stability of delayed neural network systems. For this purpose, the activation functions are assumed to be globally Lipschitz continuous. The properties of norms and the relationship of homeomorphism are adjusted to ensure the existence as well as the uniqueness of the equilibrium point. Then by employing suitable Lyapunov functional, some delay-independent sufficient conditions are derived for exponential convergence toward global equilibrium state associated with different input sources. The obtained results are shown to be more general and less restrictive than the previous results derived in the literature. Lastly, a number of examples are provided to demonstrate the validity of the results proposed.
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Metadata
Title
New conditions for global exponential stability of continuous-time neural networks with delays
Authors
Haibo Gao
Xingguo Song
Liang Ding
Deyou Liu
Minghui Hao
Publication date
01-01-2013
Publisher
Springer-Verlag
Published in
Neural Computing and Applications / Issue 1/2013
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-011-0745-9

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