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Asymptotical mean square stability of cellular neural networks with random delay

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【作者】 朱恩文； 王勇； 张汉君； 邹捷中；

【Author】 ZHU En-wen1,2,WANG Yong3,ZHANG Han-jun 2,ZOU Jie-zhong4 (1.School of Mathematics and Computational Science,Changsha Universify of Science and Technology,Changsha 410076,China; 2. School of Mathematics and Computational Science,Xiangtan University,Xiangtan 411105,China; 3. Dept. of Mathematics,Harbin Institute of Technology,Harbin 150001,China; 4. School of Mathematics,Central South University,Changsha 410075,China)

【机构】 School of Mathematics and Computational Science,Changsha Universify of Science and Technology； School of Mathematics and Computational Science,Xiangtan University； Dept. of Mathematics,Harbin Institute of Technology； School of Mathematics,Central South University；

【摘要】 In this paper,the asymptotical mean-square stability analysis problem is considered for a class of cellular neural networks (CNNs) with random delay. Compared with the previous work,the delay is modeled by a continuous-time homogeneous Markov process with a finite number of states. The main purpose of this paper is to establish easily verifiable conditions under which the random delayed cellular neural network is asymptotic mean-square stability. By using some stochastic analysis techniques and Lyapunov-Krasovskii functional,some conditions are derived to ensure that the cellular neural networks with random delay is asymptotical mean-square stability. A numerical example is exploited to show the vadlidness of the established results.

【Abstract】 In this paper,the asymptotical mean-square stability analysis problem is considered for a class of cellular neural networks (CNNs) with random delay. Compared with the previous work,the delay is modeled by a continuous-time homogeneous Markov process with a finite number of states. The main purpose of this paper is to establish easily verifiable conditions under which the random delayed cellular neural network is asymptotic mean-square stability. By using some stochastic analysis techniques and Lyapunov-Krasovskii functional,some conditions are derived to ensure that the cellular neural networks with random delay is asymptotical mean-square stability. A numerical example is exploited to show the vadlidness of the established results.

【关键词】 cellular neural networks； asymptotical mean-square stability； random delay； linear matrix inequality；
【Key words】 cellular neural networks； asymptotical mean-square stability； random delay； linear matrix inequality；
【基金】 Sponsored by the National Natural Science Foundation of China(Grant No.10771044);the Natural Science Foundation of Hunan Province(Grant No.09JJ6006);the Excellent Youth Foundation of Educational Committee of Hunan Provincial (Grant No.08B005);the Hunan Postdoctoral Scientific Pro-gram(Grant No.2009RS3020);the Scientific Research Funds of Hunan Provincial Education Department of China(Grant No.09C059)
  • 【文献出处】 Journal of Harbin Institute of Technology ,哈尔滨工业大学学报(英文版) , 编辑部邮箱 ,2010年03期
  • 【分类号】TP183
  • 【被引频次】1
  • 【下载频次】53
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