On Stability of Neural Networks by a Lyapunov Functional-Based Approach

In this paper, a new Lyapunov functional-based method is proposed for the stability analysis of delayed cellular neural networks (DCNN). Global exponential stability conditions are obtained for the general DCNN, the Hopfield neural networks (HNNs), and delayed HNNs with monotonic nondecreasing and n...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems. 1, Fundamental theory and applications Fundamental theory and applications, 2007-04, Vol.54 (4), p.912-924
Main Authors: Xu, Jun, Pi, Daoying, Cao, Yong-Yan, Zhong, Shouming
Format: Article
Language:English
Subjects:
Activation
Asymptotic stability
Cellular
Cellular neural networks
Circuits
Delay effects
Delayed cellular neural networks (DCNNs)
global exponential stability
Hopfield neural networks
Industrial control
Laboratories
Lyapunov functional
Neural networks
Signal processing
Stability
Stability analysis
Stability criteria
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Summary:In this paper, a new Lyapunov functional-based method is proposed for the stability analysis of delayed cellular neural networks (DCNN). Global exponential stability conditions are obtained for the general DCNN, the Hopfield neural networks (HNNs), and delayed HNNs with monotonic nondecreasing and nonconstant activation functions
ISSN:1549-8328
1057-7122
1558-0806
DOI:10.1109/TCSI.2007.890604