Multistability in a Multidirectional Associative Memory Neural Network with Delays

This paper focuses on the multidirectional associative memory (MAM) neural networks with m fields which is more advanced to realize associative memory. Based on the Brouwer fixed point theorem and Dini upper right derivative, it is confirmed that the multidirectional associative memory neural networ...

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Main Authors: Min Wang, Tiejun Zhou
Format: Article
Language:English
Published: Wiley 2013-01-01
Series:Journal of Applied Mathematics
Online Access:http://dx.doi.org/10.1155/2013/592056
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author Min Wang
Tiejun Zhou
author_facet Min Wang
Tiejun Zhou
author_sort Min Wang
collection DOAJ
description This paper focuses on the multidirectional associative memory (MAM) neural networks with m fields which is more advanced to realize associative memory. Based on the Brouwer fixed point theorem and Dini upper right derivative, it is confirmed that the multidirectional associative memory neural network can have equilibria and equilibria of them are stable, where l is a parameter associated with the number of neurons. Furthermore, an example is given to illustrate the effectiveness of the results.
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institution Kabale University
issn 1110-757X
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language English
publishDate 2013-01-01
publisher Wiley
record_format Article
series Journal of Applied Mathematics
spelling doaj-art-e4f1328ba23f4c4a90c6cdee8762eaba2025-08-20T03:26:25ZengWileyJournal of Applied Mathematics1110-757X1687-00422013-01-01201310.1155/2013/592056592056Multistability in a Multidirectional Associative Memory Neural Network with DelaysMin Wang0Tiejun Zhou1College of Orient Science and Technology, Hunan Agricultural University, Changsha, Hunan 410128, ChinaCollege of Science, Hunan Agricultural University, Changsha, Hunan 410128, ChinaThis paper focuses on the multidirectional associative memory (MAM) neural networks with m fields which is more advanced to realize associative memory. Based on the Brouwer fixed point theorem and Dini upper right derivative, it is confirmed that the multidirectional associative memory neural network can have equilibria and equilibria of them are stable, where l is a parameter associated with the number of neurons. Furthermore, an example is given to illustrate the effectiveness of the results.http://dx.doi.org/10.1155/2013/592056
spellingShingle Min Wang
Tiejun Zhou
Multistability in a Multidirectional Associative Memory Neural Network with Delays
Journal of Applied Mathematics
title Multistability in a Multidirectional Associative Memory Neural Network with Delays
title_full Multistability in a Multidirectional Associative Memory Neural Network with Delays
title_fullStr Multistability in a Multidirectional Associative Memory Neural Network with Delays
title_full_unstemmed Multistability in a Multidirectional Associative Memory Neural Network with Delays
title_short Multistability in a Multidirectional Associative Memory Neural Network with Delays
title_sort multistability in a multidirectional associative memory neural network with delays
url http://dx.doi.org/10.1155/2013/592056
work_keys_str_mv AT minwang multistabilityinamultidirectionalassociativememoryneuralnetworkwithdelays
AT tiejunzhou multistabilityinamultidirectionalassociativememoryneuralnetworkwithdelays