{"title":"神经网络缺陷预测","authors":"R. Stites, Bryan Ward, Robert Henry Walters","doi":"10.1145/106965.106970","DOIUrl":null,"url":null,"abstract":"The industrial and scientific world abound with problems that are poorly un&rstood or for which apparent anomalous conditions exist. Artificial Neural Networks are utilized with conventional techniques to extract salient features and relationships which are non-linear in nature. Defect causality in a large continuous flow chemical process is investigated. Significant gains in the prediction of defects over traditional statistical methods are achieved.","PeriodicalId":359315,"journal":{"name":"conference on Analysis of Neural Network Applications","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1991-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Defect prediction with neural networks\",\"authors\":\"R. Stites, Bryan Ward, Robert Henry Walters\",\"doi\":\"10.1145/106965.106970\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The industrial and scientific world abound with problems that are poorly un&rstood or for which apparent anomalous conditions exist. Artificial Neural Networks are utilized with conventional techniques to extract salient features and relationships which are non-linear in nature. Defect causality in a large continuous flow chemical process is investigated. Significant gains in the prediction of defects over traditional statistical methods are achieved.\",\"PeriodicalId\":359315,\"journal\":{\"name\":\"conference on Analysis of Neural Network Applications\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1991-05-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"conference on Analysis of Neural Network Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/106965.106970\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"conference on Analysis of Neural Network Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/106965.106970","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The industrial and scientific world abound with problems that are poorly un&rstood or for which apparent anomalous conditions exist. Artificial Neural Networks are utilized with conventional techniques to extract salient features and relationships which are non-linear in nature. Defect causality in a large continuous flow chemical process is investigated. Significant gains in the prediction of defects over traditional statistical methods are achieved.
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