基于忆阻器分数阶神经网络的全局 Mittag-Leffler 稳定性与同步。
Global Mittag-Leffler stability and synchronization of memristor-based fractional-order neural networks.
机构信息
School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China; Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Wuhan 430074, China.
School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China; Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Wuhan 430074, China.
出版信息
Neural Netw. 2014 Mar;51:1-8. doi: 10.1016/j.neunet.2013.11.016. Epub 2013 Dec 1.
The present paper introduces memristor-based fractional-order neural networks. The conditions on the global Mittag-Leffler stability and synchronization are established by using Lyapunov method for these networks. The analysis in the paper employs results from the theory of fractional-order differential equations with discontinuous right-hand sides. The obtained results extend and improve some previous works on conventional memristor-based recurrent neural networks.
本文介绍了基于忆阻器的分数阶神经网络。利用李雅普诺夫方法，针对这些网络建立了全局 Mittag-Leffler 稳定性和同步的条件。本文的分析采用了具有不连续右部的分数阶微分方程理论的结果。所得到的结果扩展和改进了一些关于传统基于忆阻器的递归神经网络的已有工作。
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