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Cogn Neurodyn. 2016 Feb;10(1):85-98. doi: 10.1007/s11571-015-9356-y. Epub 2015 Sep 29.
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Pinning synchronization of coupled inertial delayed neural networks.耦合惯性时滞神经网络的钉扎同步
Cogn Neurodyn. 2015 Jun;9(3):341-50. doi: 10.1007/s11571-014-9322-0. Epub 2014 Nov 26.
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pth moment exponential stochastic synchronization of coupled memristor-based neural networks with mixed delays via delayed impulsive control.基于忆阻器的具有混合时滞的耦合神经网络通过延迟脉冲控制实现的pth矩指数随机同步
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Passivity and Passification of Memristor-Based Recurrent Neural Networks With Additive Time-Varying Delays.基于忆阻器的具有加性时变时滞的递归神经网络的被动化与钝化。
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New synchronization criteria for memristor-based networks: adaptive control and feedback control schemes.基于忆阻器网络的新同步准则：自适应控制与反馈控制方案
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8
Exponential synchronization of memristive Cohen-Grossberg neural networks with mixed delays.具有混合时滞的忆阻 Cohen-Grossberg 神经网络的指数同步。
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Neural learning circuits utilizing nano-crystalline silicon transistors and memristors.利用纳米晶体硅晶体管和忆阻器的神经学习电路。
IEEE Trans Neural Netw Learn Syst. 2012 Apr;23(4):565-73. doi: 10.1109/TNNLS.2012.2184801.
10
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具有不同忆导和不确定时延的基于忆阻器的双向联想记忆神经网络的无源特性

Passivity of memristor-based BAM neural networks with different memductance and uncertain delays.

作者信息

Anbuvithya R, Mathiyalagan K, Sakthivel R, Prakash P

机构信息

Department of Mathematics, National Institute of Technology, Tiruchirappalli, 620 015 India.

Department of Mathematics, Anna University-Regional Centre, Coimbatore, 641 047 India.

出版信息

Cogn Neurodyn. 2016 Aug;10(4):339-51. doi: 10.1007/s11571-016-9385-1. Epub 2016 Apr 27.

DOI:10.1007/s11571-016-9385-1
PMID:27468321
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4947057/
Abstract

This paper addresses the passivity problem for a class of memristor-based bidirectional associate memory (BAM) neural networks with uncertain time-varying delays. In particular, the proposed memristive BAM neural networks is formulated with two different types of memductance functions. By constructing proper Lyapunov-Krasovskii functional and using differential inclusions theory, a new set of sufficient condition is obtained in terms of linear matrix inequalities which guarantee the passivity criteria for the considered neural networks. Finally, two numerical examples are given to illustrate the effectiveness of the proposed theoretical results.

摘要

本文研究了一类具有不确定时变延迟的基于忆阻器的双向联想记忆（BAM）神经网络的无源问题。具体而言，所提出的忆阻BAM神经网络采用了两种不同类型的忆导函数来构建。通过构造适当的Lyapunov-Krasovskii泛函并利用微分包含理论，以线性矩阵不等式的形式得到了一组新的充分条件，这些条件保证了所考虑神经网络的无源准则。最后，给出了两个数值例子来说明所提出理论结果的有效性。