Linear matrix inequality approach for robust stability analysis for stochastic neural networks with time-varying delay

doi:10.1088/1674-1056/20/4/040204

Abstract This paper studies the problem of linear matrix inequality (LMI) approach to robust stability analysis for stochastic neural networks with a time-varying delay. By developing a delay decomposition approach, the information of the delayed plant states can be taken into full consideration. Based on the new Lyapunov-Krasovskii functional, some inequality techniques and stochastic stability theory, new delay-dependent stability criteria are obtained in terms of LMIs. The proposed results prove the less conservatism, which are realized by choosing new Lyapunov matrices in the decomposed integral intervals. Finally, numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed LMI method.

Keywords: delay-dependent stability linear matrix inequality Lyapunov--Krasovskii functional stochastic neural networks

Received: 29 October 2010
Revised: 29 November 2010
Accepted manuscript online:

PACS:	02.50.Ey	(Stochastic processes)
	02.50.Fz	(Stochastic analysis)
	07.05.Mh	(Neural networks, fuzzy logic, artificial intelligence)

Fund: Project supported by the Science Foundation of the Department of Science and Technology, New Delhi, India (Grant No. SR/S4/MS:485/07).

Cite this article:

S. Lakshmanan and P. Balasubramaniam Linear matrix inequality approach for robust stability analysis for stochastic neural networks with time-varying delay 2011 Chin. Phys. B 20 040204

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Linear matrix inequality approach for robust stability analysis for stochastic neural networks with time-varying delay

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