New sufficient conditions for global robust stability of delayed neural networks

Qi, Hou-Duo (2007) New sufficient conditions for global robust stability of delayed neural networks. IEEE Transactions on Circuits and Systems Part 1: Regular Papers, 54, (5), 1131-1141.


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In this paper, we continue to explore application of nonsmooth analysis to the study of global asymptotic robust stability (GARS) of delayed neural networks. In combination with Lyapunov theory, our approach gives several new types of sufficient conditions ensuring GARS. A significant common aspect of our results is their low computational complexity. It is demonstrated that the reported results can be verified either by conducting spectral decompositions of symmetric matrices associated with the uncertainty sets of network parameters, or by solving a semidefinite programming problem. Nontrivial examples are constructed to compare with some closely related existing results

Item Type: Article
ISSNs: 1057-7122 (print)
Related URLs:
Keywords: delayed neural networks, Lyapunov function, equilibrium point, global asymptotic robust stability (GARS), nonsingularity
Subjects: Q Science > Q Science (General)
Divisions : University Structure - Pre August 2011 > School of Mathematics > Operational Research
ePrint ID: 54533
Accepted Date and Publication Date:
May 2007Published
Date Deposited: 28 Jul 2008
Last Modified: 31 Mar 2016 12:33

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