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.

Chaotic neural network for associative memory

1999 ◽  
Vol 16 (2) ◽  
pp. 130-137
Author(s):  
Yifeng Zhang ◽  
Luxi Yang ◽  
Zhenya He
Keyword(s):  
Neural Network ◽  
Associative Memory ◽  
Chaotic Neural Network

Associative memory with a controlled chaotic neural network

2008 ◽  
Vol 71 (13-15) ◽  
pp. 2794-2805 ◽  
Author(s):  
Guoguang He ◽  
Luonan Chen ◽  
Kazuyuki Aihara
Keyword(s):  
Neural Network ◽  
Associative Memory ◽  
Chaotic Neural Network

scholarly journals Design Ratio-Memory Cellular Neural Network (RMCNN) in CMOS Circuit Used in Association-Memory Applications for 0.25 mm Silicon Technology

2016 ◽  
Vol 10 (1) ◽  
pp. 54-69
Author(s):  
Jui-Lin Lai ◽  
Chung-Yu Wu
Keyword(s):  
Neural Network ◽  
Associative Memory ◽  
Hebbian Learning ◽  
Learning Algorithm ◽  
Cellular Neural Network ◽  
Cmos Technology ◽  
Neural System ◽  
Test Input ◽  
Vlsi Technology ◽  
Memory Applications

The paper is proposed the Ratio-Memory Cellular Neural Network (RMCNN) that structure with the self-feedback and the modified Hebbian learning algorithm. The learnable RMCNN architecture was designed and realized in CMOS technology for associative memory neural network applications. The exemplar patterns can be learned and correctly recognized the output patterns for the proposed system. Only self-output pixel value in A template and B template weights are updated by the nearest neighboring five elements for all test input exemplar patterns. The learned ratio weights of the B template are generated that the catch weights are performed the summation of absolute coefficients operation to enhance the feature of recognized pattern. Simulation results express that the system can be learned some exemplar patterns with noise and recognized the correctly pattern. The 9×9 RMCNN structure with self-feedback and the modified Hebbian learning algorithm is implemented and verified in the CMOS circuits for TSMC 0.25 µm 1P5M VLSI technology. The proposed RMCNN have more learning and recognition capability for the variant exemplar patterns in the auto-associative memory neural system applications.

Knowledge Processing System Using Chaotic Associative Memory

2000 ◽  
Vol 4 (1) ◽  
pp. 39-45
Author(s):  
Yuko Osana ◽  
◽  
Masafumi Hagiwara
Keyword(s):  
Neural Network ◽  
Computer Simulations ◽  
Associative Memory ◽  
Semantic Network ◽  
Processing System ◽  
External Input ◽  
Input Pattern ◽  
Chaotic Neural Network ◽  
Knowledge Processing ◽  
Noisy Input

In this paper, we propose a knowledge processing system using chaotic associative memory (KPCAM). KPCAM is based on a chaotic neural network (CAM) composed of chaotic neurons. In conventional chaotic neural network, when a stored pattern is given continuously to the network as an external input, the input pattern vicinity is searched. The CAM makes use of this property to separate superimposed patterns and to deal with many-tomany associations. In this research, the CAM is applied to knowledge processing in which knowledge is represented in a form of semantic network. The proposed KPCAM has the following features: (1) it can deal with knowledge represented in a form of semantic network; (2) it can deal with characteristic inheritance; (3) it is robust for noisy input. A series of computer simulations shows the effectiveness of the proposed system.

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