{"title":"基于SVR的传感器静态误差校正","authors":"Ding Lei","doi":"10.1109/ICNC.2012.6234714","DOIUrl":null,"url":null,"abstract":"To improve the stability of the sensor and to reduce the non-goal parameter's influence, a new static error correction method of the sensor based on support vector machine for regression (SVR) is presented. Experimental results show that the proposed method can decrease the temperature sensitivity coefficient of pressure sensor and improve the measurement accuracy of pressure validly. And judging from it's stability, it proves to be much better than traditional error correction methods.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Static error correction of the sensor based on SVR\",\"authors\":\"Ding Lei\",\"doi\":\"10.1109/ICNC.2012.6234714\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To improve the stability of the sensor and to reduce the non-goal parameter's influence, a new static error correction method of the sensor based on support vector machine for regression (SVR) is presented. Experimental results show that the proposed method can decrease the temperature sensitivity coefficient of pressure sensor and improve the measurement accuracy of pressure validly. And judging from it's stability, it proves to be much better than traditional error correction methods.\",\"PeriodicalId\":404981,\"journal\":{\"name\":\"2012 8th International Conference on Natural Computation\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-07-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 8th International Conference on Natural Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNC.2012.6234714\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 8th International Conference on Natural Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNC.2012.6234714","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Static error correction of the sensor based on SVR
To improve the stability of the sensor and to reduce the non-goal parameter's influence, a new static error correction method of the sensor based on support vector machine for regression (SVR) is presented. Experimental results show that the proposed method can decrease the temperature sensitivity coefficient of pressure sensor and improve the measurement accuracy of pressure validly. And judging from it's stability, it proves to be much better than traditional error correction methods.