{"title":"通用学习网络的自相关联想记忆","authors":"Keiko Shibuta, K. Hirasawa, Jinglu Hu, J. Murata","doi":"10.1109/SICE.2002.1195251","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a new auto correlation associative memory using Universal Learning Networks (ULNs). It enables not only to obtain associative memory by optimizing parameters but also to store more memories than conventional models by introducing \"don't care nodes\" or \"sensitivity term\". This is expected to settle some problems related to associative memory.","PeriodicalId":301855,"journal":{"name":"Proceedings of the 41st SICE Annual Conference. SICE 2002.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Auto correlation associative memory by Universal Learning Networks (ULNs)\",\"authors\":\"Keiko Shibuta, K. Hirasawa, Jinglu Hu, J. Murata\",\"doi\":\"10.1109/SICE.2002.1195251\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose a new auto correlation associative memory using Universal Learning Networks (ULNs). It enables not only to obtain associative memory by optimizing parameters but also to store more memories than conventional models by introducing \\\"don't care nodes\\\" or \\\"sensitivity term\\\". This is expected to settle some problems related to associative memory.\",\"PeriodicalId\":301855,\"journal\":{\"name\":\"Proceedings of the 41st SICE Annual Conference. SICE 2002.\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-08-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 41st SICE Annual Conference. SICE 2002.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SICE.2002.1195251\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 41st SICE Annual Conference. SICE 2002.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SICE.2002.1195251","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Auto correlation associative memory by Universal Learning Networks (ULNs)
In this paper, we propose a new auto correlation associative memory using Universal Learning Networks (ULNs). It enables not only to obtain associative memory by optimizing parameters but also to store more memories than conventional models by introducing "don't care nodes" or "sensitivity term". This is expected to settle some problems related to associative memory.
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