Global exponential stability in Lagrange sense for recurrent neural networks with time delays

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• 作者：Xiaoxin Liao ; Qi Luo ; Zhigang Zeng ; Yunxia Guo
• 关键词：Recurrent neural networks ; Lagrange stability ; Global exponential attractivity ; Delays
• 刊名：Nonlinear Analysis ; Real World Applications
• 出版年：2008
• 期刊代码：44_14681218
• 类别：mt
• 出版时间：September 2008
• 卷：9
• 期：4
• 页码：1535-1557
• 文件大小：284 K

摘要

In this paper, we study the global exponential stability in Lagrange sense for continuous recurrent neural networks (RNNs) with multiple time delays. Three different types of activation functions are considered, which include both bounded and unbounded activation functions. By constructing appropriate Lyapunov-like functions, we provide easily verifiable criteria for the boundedness and global exponential attractivity of RNNs. These results can be applied to analyze monostable as well as multistable neural networks.