{"title":"最小熵网络","authors":"R. Brause","doi":"10.1109/TAI.1992.246369","DOIUrl":null,"url":null,"abstract":"It is shown that, using as basic building block a linear neuron with an anti-Hebb rule and restricted weights, an asymmetric network which computes the eigenvectors in the ascending order of their corresponding eigenvalues can be built. The conditions for their convergence are obtained and demonstrated by simulations.<<ETX>>","PeriodicalId":265283,"journal":{"name":"Proceedings Fourth International Conference on Tools with Artificial Intelligence TAI '92","volume":"136 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"The minimum entropy network\",\"authors\":\"R. Brause\",\"doi\":\"10.1109/TAI.1992.246369\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"It is shown that, using as basic building block a linear neuron with an anti-Hebb rule and restricted weights, an asymmetric network which computes the eigenvectors in the ascending order of their corresponding eigenvalues can be built. The conditions for their convergence are obtained and demonstrated by simulations.<<ETX>>\",\"PeriodicalId\":265283,\"journal\":{\"name\":\"Proceedings Fourth International Conference on Tools with Artificial Intelligence TAI '92\",\"volume\":\"136 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1992-11-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings Fourth International Conference on Tools with Artificial Intelligence TAI '92\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TAI.1992.246369\",\"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 Fourth International Conference on Tools with Artificial Intelligence TAI '92","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TAI.1992.246369","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
It is shown that, using as basic building block a linear neuron with an anti-Hebb rule and restricted weights, an asymmetric network which computes the eigenvectors in the ascending order of their corresponding eigenvalues can be built. The conditions for their convergence are obtained and demonstrated by simulations.<>
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