基于马氏切换的时滞脉冲随机Cohen-Grossberg神经网络模型的均方指数稳定性分析

Focused on Cohen-Grossberg neural networks, this paper investigates the mean-square exponential stability by means of the vector Lyapunov function. This method ensures that the impulsive stochastic Cohen-Grossberg neural network is exponentially stable. Finally, an example is used to illustrate the...

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Bibliographic Details
Published inNanjing Xinxi Gongcheng Daxue Xuebao Vol. 9; no. 3; pp. 326 - 331
Main Authors Lei, Li, Xiuli, He
Format Journal Article
LanguageChinese
Published Nanjing Nanjing University of Information Science & Technology 01.06.2017
河海大学理学院,南京,211100
Subjects
Markov processes
Neural networks
Probability theory
Stability
Switching theory
Cohen-Grossberg网络模型
Cohen-Grossberg networks
马氏切换
mean-square exponential stability
均方指数稳定性
Markovian switching
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ISSN1674-7070
DOI10.13878/j.cnki.jnuist.2017.03.011

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Summary:Focused on Cohen-Grossberg neural networks, this paper investigates the mean-square exponential stability by means of the vector Lyapunov function. This method ensures that the impulsive stochastic Cohen-Grossberg neural network is exponentially stable. Finally, an example is used to illustrate the conclusions.
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ISSN:1674-7070
DOI:10.13878/j.cnki.jnuist.2017.03.011