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
Memristive Neural Networks: A Neuromorphic Paradigm for Extreme Learning Machine
TL;DR: Experimental results over the canonical machine learning dataset show that the memristor-based ELM achieves the same level of performance as the one implemented via traditional software, and exhibits great potential that ELM can be implemented in neromorphic computation paradigms.
Journal ArticleDOI
Global attractivity of memristor-based fractional-order neural networks
TL;DR: Using Filippov solutions, the existence of memristor-based FNN's solutions is firstly guaranteed under a growth condition, and with proposing additional conditions, the global attractivity of mem Bristor- based FNN is realized.
Journal ArticleDOI
Exponential synchronization of memristive neural networks with time delays
TL;DR: One type of new sandwich control structure is designed to ensure the exponential synchronization of two MNNs with two types of time delays via sandwich control to solve the unexpected parameter mismatch issue between driven system and response system.
Journal ArticleDOI
Stability analysis for quaternion-valued inertial memristor-based neural networks with time delays
TL;DR: Quaternion-valued inertial memristor-based neural networks with time-varying delays are found to be effective in demonstrating complex dynamic behaviors.
Journal ArticleDOI
State estimation for complex-valued memristive neural networks with time-varying delays
Runan Guo,Ziye Zhang,Ming Gao +2 more
TL;DR: In this article, a sufficient delay-dependent condition which guarantees that the error state system is global asymptotically stable is derived for the addressed system, and a suitable state estimator is also designed.
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.
Journal ArticleDOI
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.
Journal ArticleDOI
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.