Stability Analysis of Fractional-Order Hopfield Neural Networks with Time-Varying External Inputs

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• 作者：Shuo Zhang ; Yongguang Yu ; Lingling Geng
• 关键词：Stability ; Fractional ; order ; Neural networks ; Time ; varying external inputs ; Robust synchronization
• 刊名：Neural Processing Letters
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：45
• 期：1
• 页码：223-241
• 全文大小：
• 刊物类别：Physics and Astronomy
• 刊物主题：Artificial Intelligence (incl. Robotics); Complex Systems; Computational Intelligence;
• 出版者：Springer US
• ISSN：1573-773X
• 卷排序：45

文摘

Fractional-order Hopfield neural networks are frequently utilized to model the information processing of neuronal interactions. It is needed to analyze the stability of such neural systems, when their external inputs are time-varying. In this manuscript, some sufficient conditions are presented firstly for the stability of the non-autonomous fractional-order systems by employing Lyapunov functionals. In further, under time-varying external inputs, fractional-order Hopfield neural networks are regards as a class of non-autonomous fractional-order systems, whose general result is used to acquire the stability conditions of our studied neural system. Moreover, a novel robust synchronization method between such neural systems is proposed with the help of the obtained results. Furthermore, some numerical examples are provided to demonstrate the effectiveness of the presented theoretical results.