{"title":"使用n元组技术的自动关联内存","authors":"J. Bishop, R. Mitchell","doi":"10.1049/ISE.1994.0023","DOIUrl":null,"url":null,"abstract":"The use of n-tuple or weightless neural networks as pattern recognition devices has been well documented. They have a significant advantages over more common networks paradigms, such as the multilayer perceptron in that they can be easily implemented in digital hardware using standard random access memories. To date, n-tuple networks have predominantly been used as fast pattern classification devices. The paper describes how n-tuple techniques can be used in the hardware implementation of a general auto-associative network.","PeriodicalId":55165,"journal":{"name":"Engineering Intelligent Systems for Electrical Engineering and Communications","volume":"148 1","pages":"222-229"},"PeriodicalIF":0.0000,"publicationDate":"1994-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Auto-associative memory using n-tuple techniques\",\"authors\":\"J. Bishop, R. Mitchell\",\"doi\":\"10.1049/ISE.1994.0023\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The use of n-tuple or weightless neural networks as pattern recognition devices has been well documented. They have a significant advantages over more common networks paradigms, such as the multilayer perceptron in that they can be easily implemented in digital hardware using standard random access memories. To date, n-tuple networks have predominantly been used as fast pattern classification devices. The paper describes how n-tuple techniques can be used in the hardware implementation of a general auto-associative network.\",\"PeriodicalId\":55165,\"journal\":{\"name\":\"Engineering Intelligent Systems for Electrical Engineering and Communications\",\"volume\":\"148 1\",\"pages\":\"222-229\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Engineering Intelligent Systems for Electrical Engineering and Communications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1049/ISE.1994.0023\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Intelligent Systems for Electrical Engineering and Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1049/ISE.1994.0023","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The use of n-tuple or weightless neural networks as pattern recognition devices has been well documented. They have a significant advantages over more common networks paradigms, such as the multilayer perceptron in that they can be easily implemented in digital hardware using standard random access memories. To date, n-tuple networks have predominantly been used as fast pattern classification devices. The paper describes how n-tuple techniques can be used in the hardware implementation of a general auto-associative network.