Journal ArticleDOI
Dynamic behaviors of memristor-based recurrent neural networks with time-varying delays
Ailong Wu,Zhigang Zeng +1 more
TLDR
A general class of memristor-based recurrent neural networks with time-varying delays with exponential convergence and conditions on the nondivergence and global attractivity are established by using local inhibition.About:
This article is published in Neural Networks.The article was published on 2012-12-01. It has received 177 citations till now. The article focuses on the topics: Recurrent neural network & Memristor.read more
Citations
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Journal ArticleDOI
Exponential synchronization of delayed memristor-based chaotic neural networks via periodically intermittent control.
Guodong Zhang,Yi Shen +1 more
TL;DR: This paper investigates the exponential synchronization of coupled memristor-based chaotic neural networks with both time-varying delays and general activation functions and adopts nonsmooth analysis and control theory to handle memrist or chaotic networks with discontinuous right-hand side.
Journal ArticleDOI
Global exponential dissipativity and stabilization of memristor-based recurrent neural networks with time-varying delays
TL;DR: It is proven herein that there are 2(2n(2)-n) equilibria for an n-neuron memristor-based neural network and they are located in the derived globally attractive sets.
Journal ArticleDOI
Robust Synchronization of Multiple Memristive Neural Networks With Uncertain Parameters via Nonlinear Coupling
TL;DR: A pinning adaptive coupling method is proposed to ensure global synchronization without knowing the bound of parameter uncertainties, and a set of sufficient conditions are derived based on the Lyapunov stability theory for ascertaining global robust synchronization of coupled MMNNs.
Journal ArticleDOI
Lagrange Stability of Memristive Neural Networks With Discrete and Distributed Delays
Ailong Wu,Zhigang Zeng +1 more
TL;DR: Several succinct criteria are provided to ascertain the Lagrange stability of memristive neural networks with and without delays, and three numerical examples are given to show the superiority of theoretical results.
Journal ArticleDOI
Attractivity Analysis of Memristor-Based Cellular Neural Networks With Time-Varying Delays
Zhenyuan Guo,Jun Wang,Zheng Yan +2 more
TL;DR: It is shown that the number of equilibria located in the saturation regions of the piecewise-linear activation functions of an n-neuron MCNN with time-varying delays increases significantly from 2n to 22n2+n (22n2 times) compared with that without a memristor.
References
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Journal ArticleDOI
The missing memristor found
TL;DR: It is shown, using a simple analytical example, that memristance arises naturally in nanoscale systems in which solid-state electronic and ionic transport are coupled under an external bias voltage.
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Memristor-The missing circuit element
TL;DR: In this article, the memristor is introduced as the fourth basic circuit element and an electromagnetic field interpretation of this relationship in terms of a quasi-static expansion of Maxwell's equations is presented.
Book
Differential Equations with Discontinuous Righthand Sides
TL;DR: The kind and level of sophistication of mathematics applied in various sciences has changed drastically in recent years: measure theory is used (non-trivially) in regional and theoretical economics, algebraic geometry interacts with physics, and such new emerging subdisciplines as "experimental mathematics", "CFD", "completely integrable systems", "chaos, synergetics and large-scale order", which are almost impossible to fit into the existing classification schemes.
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Circuit Elements With Memory: Memristors, Memcapacitors, and Meminductors
TL;DR: It is argued that capacitive and inductive elements, namely, capacitors and inductors whose properties depend on the state and history of the system, are common at the nanoscale, where the dynamical properties of electrons and ions are likely to depend upon the history ofThe system, at least within certain time scales.
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Experimental demonstration of associative memory with memristive neural networks
TL;DR: This work has demonstrated experimentally the formation of associative memory in a simple neural network consisting of three electronic neurons connected by two memristor-emulator synapses and opens up new possibilities in the understanding of neural processes using memory devices.