{"title":"基于神经网络和模糊规则的木板分类","authors":"C.A. de Franca, A. Gonzaga, A. Slaets","doi":"10.1109/CYBVIS.1996.629462","DOIUrl":null,"url":null,"abstract":"Fuzzy-neural systems have been applied to many engineering tasks. Fuzzy neurons in pattern classification are extremely useful because they provide a degree of membership information instead of numerical critic values such as \"0\" (bad) or \"1\" (good). This paper describes a neural network application for automatic classification of wooden boards. The basic processing unit consists of two types of generic OR and AND neurons structured in a four layer topology.","PeriodicalId":103287,"journal":{"name":"Proceedings II Workshop on Cybernetic Vision","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Classification of wooden boards by neural networks and fuzzy rules\",\"authors\":\"C.A. de Franca, A. Gonzaga, A. Slaets\",\"doi\":\"10.1109/CYBVIS.1996.629462\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Fuzzy-neural systems have been applied to many engineering tasks. Fuzzy neurons in pattern classification are extremely useful because they provide a degree of membership information instead of numerical critic values such as \\\"0\\\" (bad) or \\\"1\\\" (good). This paper describes a neural network application for automatic classification of wooden boards. The basic processing unit consists of two types of generic OR and AND neurons structured in a four layer topology.\",\"PeriodicalId\":103287,\"journal\":{\"name\":\"Proceedings II Workshop on Cybernetic Vision\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1996-12-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings II Workshop on Cybernetic Vision\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CYBVIS.1996.629462\",\"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 II Workshop on Cybernetic Vision","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CYBVIS.1996.629462","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Classification of wooden boards by neural networks and fuzzy rules
Fuzzy-neural systems have been applied to many engineering tasks. Fuzzy neurons in pattern classification are extremely useful because they provide a degree of membership information instead of numerical critic values such as "0" (bad) or "1" (good). This paper describes a neural network application for automatic classification of wooden boards. The basic processing unit consists of two types of generic OR and AND neurons structured in a four layer topology.
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