Gui-fang Wu, Se-young Jang, Hoon-Sung Kwak, Jie-xin Pu
{"title":"一种新的神经网络参数选择方法","authors":"Gui-fang Wu, Se-young Jang, Hoon-Sung Kwak, Jie-xin Pu","doi":"10.1109/MUE.2008.99","DOIUrl":null,"url":null,"abstract":"A new parameter selection method of neural network is presented after researching parameter selection method of neural network deeply, and it is applied to surface defect online inspection system of cold rolled strips. The method exerted the merits of small-samples fully which was utilized to train and test neural network by every possible combination of parameters to get a group of neural network parameters by plotting histogram of recognition rate under different parameter combinations, and the parameters are regarded as optimized parameters of neural network. Experiments showed that a best recognition effect by using the parameters for neural network which are selected by the new parameter selection method can be achieved among all the parameters selected randomly for surface defect of cold rolled strips.","PeriodicalId":203066,"journal":{"name":"2008 International Conference on Multimedia and Ubiquitous Engineering (mue 2008)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A New Parameter Selection Method of Neural Network\",\"authors\":\"Gui-fang Wu, Se-young Jang, Hoon-Sung Kwak, Jie-xin Pu\",\"doi\":\"10.1109/MUE.2008.99\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A new parameter selection method of neural network is presented after researching parameter selection method of neural network deeply, and it is applied to surface defect online inspection system of cold rolled strips. The method exerted the merits of small-samples fully which was utilized to train and test neural network by every possible combination of parameters to get a group of neural network parameters by plotting histogram of recognition rate under different parameter combinations, and the parameters are regarded as optimized parameters of neural network. Experiments showed that a best recognition effect by using the parameters for neural network which are selected by the new parameter selection method can be achieved among all the parameters selected randomly for surface defect of cold rolled strips.\",\"PeriodicalId\":203066,\"journal\":{\"name\":\"2008 International Conference on Multimedia and Ubiquitous Engineering (mue 2008)\",\"volume\":\"36 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-04-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 International Conference on Multimedia and Ubiquitous Engineering (mue 2008)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MUE.2008.99\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 International Conference on Multimedia and Ubiquitous Engineering (mue 2008)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MUE.2008.99","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A New Parameter Selection Method of Neural Network
A new parameter selection method of neural network is presented after researching parameter selection method of neural network deeply, and it is applied to surface defect online inspection system of cold rolled strips. The method exerted the merits of small-samples fully which was utilized to train and test neural network by every possible combination of parameters to get a group of neural network parameters by plotting histogram of recognition rate under different parameter combinations, and the parameters are regarded as optimized parameters of neural network. Experiments showed that a best recognition effect by using the parameters for neural network which are selected by the new parameter selection method can be achieved among all the parameters selected randomly for surface defect of cold rolled strips.