Associative memory design for 256 gray-level images using a multilayer neural network

Costantini G.; Casali D.; Perfetti R.

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Associative memory design for 256 gray-level images using a multilayer neural network

机译：使用多层神经网络的256个灰度图像的联想记忆设计

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A design procedure is presented for neural associative memories storing gray-scale images. It is an evolution of a previous work based on the decomposition of the image with 2/sup L/ gray levels into L binary patterns, stored in L uncoupled neural networks. In this letter, an L-layer neural network is proposed with both intralayer and interlayer connections. The connections between different layers introduce interactions among all the neurons, increasing the recall performance with respect to the uncoupled case. In particular, the proposed network can store images with the commonly used number of 256 gray levels instead of 16, as in the previous approach.

机译：提出了一种用于存储灰度图像的神经联想存储器的设计程序。它是基于以前的工作的演变，该工作基于将2 / sup L /灰度级的图像分解为L个二进制模式，并存储在L个未耦合的神经网络中。在这封信中，提出了具有层内和层间连接的L层神经网络。不同层之间的连接会引入所有神经元之间的交互作用，从而提高未耦合情况下的召回性能。特别地，如先前的方法那样，所提出的网络可以存储具有256个灰度级而不是16个灰度级的常用图像。

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《IEEE Transactions on Neural Networks》 |2006年第2期|p.519-522|共4页
作者
Costantini G.; Casali D.; Perfetti R.;
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正文语种 eng
中图分类自动化技术、计算机技术;
关键词
content-addressable storage; neural nets; 2/sup L/ gray levels; L binary patterns; L uncoupled neural networks; gray-level images; multilayer neural network; neural associative memory design; Associative memories; brain-state-in-a-box (BSB) neural networks; gray-s;

机译：内容可寻址存储;神经网络;2 / sup L /灰度级;L二进制模式;L非耦合神经网络;灰度级图像;多层神经网络;神经联想记忆设计;联想记忆;大脑中的状态盒（BSB）神经网络;灰色;

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1. Locally Connected Hopfield Multilayer Network for Associative Memories Storing 256 Levels Grey-Scale Images [J] . GIOVANNI COSTANTINI WSEAS Transactions on Circuits and Systems . 2006,第7期

机译：本地连接的Hopfield多层网络，用于存储256级灰度图像的关联内存
2. Multilayer feed forward neural networks for non-linear continuous bidirectional associative memory [J] . Singh Manu Pratap, Saraswat V. K. Applied Soft Computing . 2017,第期

机译：多层馈送非线性连续双向关联内存的前向神经网络
3. Noise supplement learning algorithm for associative memories using quantized multilayer perceptrons and sparsely interconnected neural networks [J] . Takeshi Kamio, Yusuke Magori, Mititada Morisue 電子情報通信学会技術研究報告. 非線形問題. Nonlinear Problems . 2002,第724期

机译：基于量化多层感知器和稀疏互连神经网络的联想记忆噪声补充学习算法
4. Noise Supplement Learning Algorithm for Associative Memories Using Multilayer Perceptrons and Sparsely Interconnected Neural Networks [C] . Yusuke MAGORI, Takeshi KAMIO, Hisato FUJISAKA, International Conference on Neural Networks . 2003

机译：利用多层认识与稀疏互联的神经网络的关联存储噪声补充学习算法
5. Information-theoretics based genetic algorithm: Application to Hopfield's associative memory model of neural networks [D] . Arredondo, Tomas Vidal 1997

机译：基于信息论的遗传算法：在神经网络Hopfield联想记忆模型中的应用
6. Rapid Airplane Detection in Remote Sensing Images Based on Multilayer Feature Fusion in Fully Convolutional Neural Networks [O] . Yuelei Xu, Mingming Zhu, Peng Xin, 2018

机译：全卷积神经网络中基于多层特征融合的遥感图像快速飞机检测
7. Associative Memory Design for 256 Gray-Level Images Using a Multilayer Neural Network [O] . G. Costantini, D. Casali, R. Perfetti 2006

机译：使用多层神经网络的256个灰度图像关联内存设计
8. Neural Network Computing Architectures of Coupled Associative Memories withDynamic Attractors [R] . Hirsch, M. W., Baird, B. 1996

机译：耦合联想记忆与动态吸引子的神经网络计算结构

Associative memory design for 256 gray-level images using a multilayer neural network

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