Constructing associative memories using neural networks

X. Xu; W. T. Tsai

doi:10.1016/0893-6080(90)90073-T

Constructing associative memories using neural networks

X. Xu^*
, W. T. Tsai

^*此作品的通讯作者

University of Minnesota Twin Cities

科研成果: 期刊稿件 › 文章 › 同行评审

12 引用（Scopus）

摘要

This paper proposes using a generalized Hopfield's model, also known as the McCulloch-Pitts model, as associative memories. The system always converges to the nearest stored words. This is not true for Hopfield's model. We also determine the information capacity of this generalized model. Our result is better than those in the literature.

源语言	英语
页（从-至）	301-309
页数	9
期刊	Neural Networks
卷	3
期	3
DOI	https://doi.org/10.1016/0893-6080(90)90073-T
出版状态	已出版 - 1990
已对外发布	是

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@article{ac2324ad9b1a4f35b8165630ba324d19,

title = "Constructing associative memories using neural networks",

abstract = "This paper proposes using a generalized Hopfield's model, also known as the McCulloch-Pitts model, as associative memories. The system always converges to the nearest stored words. This is not true for Hopfield's model. We also determine the information capacity of this generalized model. Our result is better than those in the literature.",

keywords = "Associative memory, Memory capacity, Neural network model",

author = "X. Xu and Tsai, \{W. T.\}",

year = "1990",

doi = "10.1016/0893-6080(90)90073-T",

language = "英语",

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TY - JOUR

T1 - Constructing associative memories using neural networks

AU - Xu, X.

AU - Tsai, W. T.

PY - 1990

Y1 - 1990

N2 - This paper proposes using a generalized Hopfield's model, also known as the McCulloch-Pitts model, as associative memories. The system always converges to the nearest stored words. This is not true for Hopfield's model. We also determine the information capacity of this generalized model. Our result is better than those in the literature.

AB - This paper proposes using a generalized Hopfield's model, also known as the McCulloch-Pitts model, as associative memories. The system always converges to the nearest stored words. This is not true for Hopfield's model. We also determine the information capacity of this generalized model. Our result is better than those in the literature.

KW - Associative memory

KW - Memory capacity

KW - Neural network model

UR - https://www.scopus.com/pages/publications/0025232994

U2 - 10.1016/0893-6080(90)90073-T

DO - 10.1016/0893-6080(90)90073-T

M3 - 文章

AN - SCOPUS:0025232994

SN - 0893-6080

VL - 3

SP - 301

EP - 309

JO - Neural Networks

JF - Neural Networks

IS - 3

ER -

Constructing associative memories using neural networks

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