{"title":"Several symmetry properties of discrete Hopfield neural networks","authors":"Jiyang Dong, Jun-ying Zhang","doi":"10.1109/ICMLC.2002.1167431","DOIUrl":null,"url":null,"abstract":"Symmetry is powerful tool to reduce the freedom of a problem. Discrete Hopfield neural networks with Hebbian learning are studied by the method of group theory in this paper, and several symmetry properties of the network being an auto-associator are given and proved.","PeriodicalId":90702,"journal":{"name":"Proceedings. International Conference on Machine Learning and Cybernetics","volume":"92 1","pages":"1374-1378 vol.3"},"PeriodicalIF":0.0000,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. International Conference on Machine Learning and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLC.2002.1167431","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Symmetry is powerful tool to reduce the freedom of a problem. Discrete Hopfield neural networks with Hebbian learning are studied by the method of group theory in this paper, and several symmetry properties of the network being an auto-associator are given and proved.
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