Global exponential stability for quaternion-valued recurrent neural networks with time-varying delays

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• 作者：Yang Liu ; Dandan Zhang ; Jianquan Lu
• 关键词：Global exponential stability ; Quaternion ; Recurrent neural network ; Time delay
• 刊名：Nonlinear Dynamics
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：87
• 期：1
• 页码：553-565
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Vibration, Dynamical Systems, Control; Classical Mechanics; Mechanical Engineering; Automotive Engineering;
• 出版者：Springer Netherlands
• ISSN：1573-269X
• 卷排序：87

文摘

In this paper, we employ a novel method for solving the problem of the global exponential stability of quaternion-valued recurrent neural networks (QVNNs) with time-varying delays. Theoretically, a QVNN can be separated into four real-valued systems, forming an equivalent real-valued system. From the view of matrix measure, based on Halanay inequality instead of Lyapunov function, some sufficient conditions are derived to guarantee the global exponential stability for QVNNs. Moreover, the activation functions are not assumed to be derivative any more, which makes the analytical procedure compact. Finally, a numerical example is provided to validate the advantage of the proposed method and to show the effectiveness of the main results.