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

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

中国物理B ›› 2010, Vol. 19 ›› Issue (6): 60512-060512.doi: 10.1088/1674-1056/19/6/060512

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

籍艳, 崔宝同

School of Communication and Control Engineering, Jiangnan University, Wuxi 214122, China

收稿日期:2009-05-17 出版日期:2010-06-15 发布日期:2010-06-15
基金资助:
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).

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

Ji Yan(籍艳)^† and Cui Bao-Tong(崔宝同)

School of Communication and Control Engineering, Jiangnan University, Wuxi 214122, China

Received:2009-05-17 Online:2010-06-15 Published:2010-06-15
Supported by:
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).

摘要/Abstract

摘要： 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.

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.

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

中图分类号: (Neural networks)

84.35.+i

02.30.Yy (Control theory) 02.50.Ga (Markov processes) 02.10.Yn (Matrix theory)

籍艳, 崔宝同. Improved delay-dependent globally asymptotic stability of delayed uncertain recurrent neural networks with Markovian jumping parameters[J]. 中国物理B, 2010, 19(6): 60512-060512.

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[J]. Chin. Phys. B, 2010, 19(6): 60512-060512.

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

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

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