{"title":"一种新的在线最小二乘支持向量机算法在瓦斯预测中的研究","authors":"Xiao-hu Zhao, Ke-ke Zhao","doi":"10.1109/INDIN.2008.4618253","DOIUrl":null,"url":null,"abstract":"This paper studied on time series prediction, and proposes a new prediction algorithm of LS-SVM online learning against the shortcomings in the traditional online learning with least squares support vector machine. This algorithm was researched and used in coal mine gas prediction and had proved effective, compared with the actual data and other relative algorithms.","PeriodicalId":112553,"journal":{"name":"2008 6th IEEE International Conference on Industrial Informatics","volume":"88 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Study of a new online Least Squares Support Vector Machine algorithm in gas prediction\",\"authors\":\"Xiao-hu Zhao, Ke-ke Zhao\",\"doi\":\"10.1109/INDIN.2008.4618253\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper studied on time series prediction, and proposes a new prediction algorithm of LS-SVM online learning against the shortcomings in the traditional online learning with least squares support vector machine. This algorithm was researched and used in coal mine gas prediction and had proved effective, compared with the actual data and other relative algorithms.\",\"PeriodicalId\":112553,\"journal\":{\"name\":\"2008 6th IEEE International Conference on Industrial Informatics\",\"volume\":\"88 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-07-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 6th IEEE International Conference on Industrial Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INDIN.2008.4618253\",\"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 6th IEEE International Conference on Industrial Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INDIN.2008.4618253","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Study of a new online Least Squares Support Vector Machine algorithm in gas prediction
This paper studied on time series prediction, and proposes a new prediction algorithm of LS-SVM online learning against the shortcomings in the traditional online learning with least squares support vector machine. This algorithm was researched and used in coal mine gas prediction and had proved effective, compared with the actual data and other relative algorithms.