Improved delay-dependent globally asymptotic stability of delayed uncertain recurrent neural networks with Markovian jumping parameters

doi:10.1088/1674-1056/19/6/060512

Abstract In this paper, we have improved delay-dependent stability criteria for recurrent neural networks with a delay varying over a range and Markovian jumping parameters. The criteria improve over some previous ones in that they have fewer matrix variables yet less conservatism. In addition, a numerical example is provided to illustrate the applicability of the result using the linear matrix inequality toolbox in MATLAB.

Keywords: recurrent neural networks time-varying delays linear matrix inequality Lyapunov--Krasovskii functional Markovian jumping parameters

Received: 17 May 2009
Accepted manuscript online:

PACS:	84.35.+i	(Neural networks)
	02.30.Yy	(Control theory)
	02.50.Ga	(Markov processes)
	02.10.Yn	(Matrix theory)

Fund: Project supported by the National Natural Science Foundation of China (Grant No.~60674026) and the Jiangsu Provincial Natural Science Foundation of China (Grant No.~BK2007016).

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Ji Yan(籍艳) and Cui Bao-Tong(崔宝同) Improved delay-dependent globally asymptotic stability of delayed uncertain recurrent neural networks with Markovian jumping parameters 2010 Chin. Phys. B 19 060512

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Improved delay-dependent globally asymptotic stability of delayed uncertain recurrent neural networks with Markovian jumping parameters

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