Transversal homoclinic orbits in a transiently chaotic neural network

2002 ◽  
Vol 12 (3) ◽  
pp. 654-671 ◽  
Author(s):  
Shyan-Shiou Chen ◽  
Chih-Wen Shih
Keyword(s):  
Neural Network ◽  
Homoclinic Orbits ◽  
Chaotic Neural Network ◽  
Transversal Homoclinic Orbits

scholarly journals Prediction Model of Weekly Retail Price for Eggs Based on Chaotic Neural Network

2013 ◽  
Vol 12 (12) ◽  
pp. 2292-2299 ◽  
Author(s):  
Zhe-min LI ◽  
Li-guo CUI ◽  
Shi-wei XU ◽  
Ling-yun WENG ◽  
Xiao-xia DONG ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Retail Price ◽  
Chaotic Neural Network

A novel memristive electronic synapse-based Hermite chaotic neural network with application in cryptography

2015 ◽  
Vol 166 ◽  
pp. 487-495 ◽  
Author(s):  
Xinli Shi ◽  
Shukai Duan ◽  
Lidan Wang ◽  
Tingwen Huang ◽  
Chuandong Li
Keyword(s):  
Neural Network ◽  
Chaotic Neural Network ◽  
Electronic Synapse

scholarly journals A Novel Chaotic Neural Network Using Memristive Synapse with Applications in Associative Memory

2012 ◽  
Vol 2012 ◽  
pp. 1-19 ◽  
Author(s):  
Xiaofang Hu ◽  
Shukai Duan ◽  
Lidan Wang
Keyword(s):  
Neural Network ◽  
Associative Memory ◽  
Large Scale ◽  
Cmos Technology ◽  
Chaotic Neural Network ◽  
Circuit Element ◽  
Nanoscale Cmos ◽  
Dynamical Behaviors ◽  
Implementation Scheme ◽  
Further Development

Chaotic Neural Network, also denoted by the acronym CNN, has rich dynamical behaviors that can be harnessed in promising engineering applications. However, due to its complex synapse learning rules and network structure, it is difficult to update its synaptic weights quickly and implement its large scale physical circuit. This paper addresses an implementation scheme of a novel CNN with memristive neural synapses that may provide a feasible solution for further development of CNN. Memristor, widely known as the fourth fundamental circuit element, was theoretically predicted by Chua in 1971 and has been developed in 2008 by the researchers in Hewlett-Packard Laboratory. Memristor based hybrid nanoscale CMOS technology is expected to revolutionize the digital and neuromorphic computation. The proposed memristive CNN has four significant features: (1) nanoscale memristors can simplify the synaptic circuit greatly and enable the synaptic weights update easily; (2) it can separate stored patterns from superimposed input; (3) it can deal with one-to-many associative memory; (4) it can deal with many-to-many associative memory. Simulation results are provided to illustrate the effectiveness of the proposed scheme.

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