时滞忆阻神经网络的指数被动性。
Exponential passivity of memristive neural networks with time delays.
机构信息
College of Mathematics and Statistics, Hubei Normal University, Huangshi 435002, China; Institute for Information and System Science, Xi'an Jiaotong University, Xi'an 710049, China; School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China.
出版信息
Neural Netw. 2014 Jan;49:11-8. doi: 10.1016/j.neunet.2013.09.002. Epub 2013 Sep 18.
Memristive neural networks are studied across many fields of science. To uncover their structural design principles, the paper introduces a general class of memristive neural networks with time delays. Passivity analysis is conducted by constructing suitable Lyapunov functional. The analysis in the paper employs the results from the theories of nonsmooth analysis and linear matrix inequalities. A numerical example is provided to illustrate the effectiveness and less conservatism of the proposed results.
忆阻神经网络在许多科学领域都有研究。为了揭示它们的结构设计原则，本文提出了一类具有时滞的通用忆阻神经网络。通过构造合适的李雅普诺夫函数进行无源分析。本文的分析利用了非光滑分析和线性矩阵不等式理论的结果。通过数值实例说明了所提出结果的有效性和较小的保守性。
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