On permutation symmetries of hopfield model neural network

Discrete Hopfield neural network (DHNN) is studied by performing permutation operations on the synaptic weight matrix. The storable patterns set stored with Hebbian learning algorithm in a network without losing memories is studied, and a condition which makes sure all the patterns of the storable...

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Main Authors:	Jiyang Dong, Shenchu Xu, Zhenxiang Chen, Boxi Wu
Format:	Article
Language:	English
Published:	Hindawi Limited 2001-01-01
Series:	Discrete Dynamics in Nature and Society
Subjects:	Discrete hopfield neural network; Permutation symmetries; Associative memory; Storable patterns set.
Online Access:	http://dx.doi.org/10.1155/S1026022601000139

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spelling	doaj-5b7b579e47ad486e993f176c6ae6d4912020-11-24T21:33:43ZengHindawi LimitedDiscrete Dynamics in Nature and Society1026-02261607-887X2001-01-016212913610.1155/S1026022601000139On permutation symmetries of hopfield model neural networkJiyang Dong0Shenchu Xu1Zhenxiang Chen2Boxi Wu3Department of Physics, Xiamen University, Xiamen 361005, ChinaDepartment of Physics, Xiamen University, Xiamen 361005, ChinaDepartment of Physics, Xiamen University, Xiamen 361005, ChinaDepartment of Physics, Xiamen University, Xiamen 361005, ChinaDiscrete Hopfield neural network (DHNN) is studied by performing permutation operations on the synaptic weight matrix. The storable patterns set stored with Hebbian learning algorithm in a network without losing memories is studied, and a condition which makes sure all the patterns of the storable patterns set have a same basin size of attraction is proposed. Then, the permutation symmetries of the network are studied associating with the stored patterns set. A construction of the storable patterns set satisfying that condition is achieved by consideration of their invariance under a point group.http://dx.doi.org/10.1155/S1026022601000139Discrete hopfield neural network; Permutation symmetries; Associative memory; Storable patterns set.
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Jiyang Dong Shenchu Xu Zhenxiang Chen Boxi Wu
spellingShingle	Jiyang Dong Shenchu Xu Zhenxiang Chen Boxi Wu On permutation symmetries of hopfield model neural network Discrete Dynamics in Nature and Society Discrete hopfield neural network; Permutation symmetries; Associative memory; Storable patterns set.
author_facet	Jiyang Dong Shenchu Xu Zhenxiang Chen Boxi Wu
author_sort	Jiyang Dong
title	On permutation symmetries of hopfield model neural network
title_short	On permutation symmetries of hopfield model neural network
title_full	On permutation symmetries of hopfield model neural network
title_fullStr	On permutation symmetries of hopfield model neural network
title_full_unstemmed	On permutation symmetries of hopfield model neural network
title_sort	on permutation symmetries of hopfield model neural network
publisher	Hindawi Limited
series	Discrete Dynamics in Nature and Society
issn	1026-0226 1607-887X
publishDate	2001-01-01
description	Discrete Hopfield neural network (DHNN) is studied by performing permutation operations on the synaptic weight matrix. The storable patterns set stored with Hebbian learning algorithm in a network without losing memories is studied, and a condition which makes sure all the patterns of the storable patterns set have a same basin size of attraction is proposed. Then, the permutation symmetries of the network are studied associating with the stored patterns set. A construction of the storable patterns set satisfying that condition is achieved by consideration of their invariance under a point group.
topic	Discrete hopfield neural network; Permutation symmetries; Associative memory; Storable patterns set.
url	http://dx.doi.org/10.1155/S1026022601000139
work_keys_str_mv	AT jiyangdong onpermutationsymmetriesofhopfieldmodelneuralnetwork AT shenchuxu onpermutationsymmetriesofhopfieldmodelneuralnetwork AT zhenxiangchen onpermutationsymmetriesofhopfieldmodelneuralnetwork AT boxiwu onpermutationsymmetriesofhopfieldmodelneuralnetwork
_version_	1725952305579163648

On permutation symmetries of hopfield model neural network

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