{"title":"基于BP神经网络的x射线钢丝绳输送带图像缺陷识别","authors":"Wang Wen, Miao Chang-yun, Wang Ji, Li Xian-guo","doi":"10.1109/ISCCS.2011.53","DOIUrl":null,"url":null,"abstract":"BP neural network is used to recognize X-ray steel rope cord conveyer belt image with defect in this paper. Firstly, the model of three layers BP neural network is established, and it is made up of 240 input nodes, 20 hidden layer nodes, and 1 output node. Then, the BP neural network is trained and tested in MATLAB. The results show that X-ray steel rope cord conveyer belt image with defect can be identified by the neural network.","PeriodicalId":326328,"journal":{"name":"2011 International Symposium on Computer Science and Society","volume":"74 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Defect Recognition of X-Ray Steel Rope Cord Conveyer Belt Image Based on BP Neural Network\",\"authors\":\"Wang Wen, Miao Chang-yun, Wang Ji, Li Xian-guo\",\"doi\":\"10.1109/ISCCS.2011.53\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"BP neural network is used to recognize X-ray steel rope cord conveyer belt image with defect in this paper. Firstly, the model of three layers BP neural network is established, and it is made up of 240 input nodes, 20 hidden layer nodes, and 1 output node. Then, the BP neural network is trained and tested in MATLAB. The results show that X-ray steel rope cord conveyer belt image with defect can be identified by the neural network.\",\"PeriodicalId\":326328,\"journal\":{\"name\":\"2011 International Symposium on Computer Science and Society\",\"volume\":\"74 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-07-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 International Symposium on Computer Science and Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCCS.2011.53\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 International Symposium on Computer Science and Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCCS.2011.53","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Defect Recognition of X-Ray Steel Rope Cord Conveyer Belt Image Based on BP Neural Network
BP neural network is used to recognize X-ray steel rope cord conveyer belt image with defect in this paper. Firstly, the model of three layers BP neural network is established, and it is made up of 240 input nodes, 20 hidden layer nodes, and 1 output node. Then, the BP neural network is trained and tested in MATLAB. The results show that X-ray steel rope cord conveyer belt image with defect can be identified by the neural network.
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