Mean-square global exponential stability in Lagrange sense for delayed recurrent neural networks with Markovian switching

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• 作者：Qiuxin Chen^aAuthor Vitae ; Lei Liu^a ; ^{liulei_hust@hhu.edu.cn}Author Vitae ; Ailong Wu^bAuthor Vitae
• 关键词：Delayed recurrent neural networks ; Mean-square exponentialstability ; Lagrange stability ; Global exponentialattractive ; Markovian chain ; M-matrix
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：22 February 2017
• 年：2017
• 卷：226
• 期：Complete
• 页码：58-65
• 全文大小：1560 K
• 卷排序：226

文摘

The paper discusses the mean-square global exponential stability in Lagrange sense for delayed recurrent neural networks (DRNNs) with Markovian switching. Two different types of activation functions are considered, which include both bounded and unbounded activation functions. By using the vector Lyapunov function and stochastic analysis technique, we establish two LL-operator differential inequalities. By employing the two LL-operator differential inequalities and vector Lyapunov functions methods, we provide easily verifiable criteria for Lagrange stability in mean-square sense of DRNNs with Markovian switching. Finally, two numerical examples are given to illustrate the efficiency of the derived results.