{"title":"具有动态信道的独立非高斯信号的直接盲分离","authors":"Ruey-Wen Liu, Hui Luo","doi":"10.1109/CNNA.1998.685325","DOIUrl":null,"url":null,"abstract":"A fundamental theorem of direct blind separation of independent non-Gaussian signals with dynamic channels is presented. Roughly, it states that blind signal separation is achieved if and only if the output signals are temporally uncorrelated and pairwise independent. This condition is simple enough to be adaptable by a neural network.","PeriodicalId":171485,"journal":{"name":"1998 Fifth IEEE International Workshop on Cellular Neural Networks and their Applications. Proceedings (Cat. No.98TH8359)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1998-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":"{\"title\":\"Direct blind separation of independent non-Gaussian signals with dynamic channels\",\"authors\":\"Ruey-Wen Liu, Hui Luo\",\"doi\":\"10.1109/CNNA.1998.685325\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A fundamental theorem of direct blind separation of independent non-Gaussian signals with dynamic channels is presented. Roughly, it states that blind signal separation is achieved if and only if the output signals are temporally uncorrelated and pairwise independent. This condition is simple enough to be adaptable by a neural network.\",\"PeriodicalId\":171485,\"journal\":{\"name\":\"1998 Fifth IEEE International Workshop on Cellular Neural Networks and their Applications. Proceedings (Cat. No.98TH8359)\",\"volume\":\"29 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1998-04-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"20\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"1998 Fifth IEEE International Workshop on Cellular Neural Networks and their Applications. Proceedings (Cat. No.98TH8359)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CNNA.1998.685325\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"1998 Fifth IEEE International Workshop on Cellular Neural Networks and their Applications. Proceedings (Cat. No.98TH8359)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CNNA.1998.685325","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Direct blind separation of independent non-Gaussian signals with dynamic channels
A fundamental theorem of direct blind separation of independent non-Gaussian signals with dynamic channels is presented. Roughly, it states that blind signal separation is achieved if and only if the output signals are temporally uncorrelated and pairwise independent. This condition is simple enough to be adaptable by a neural network.