{"title":"工业填料自适应控制的鲁棒回归","authors":"F. Denaro, L. Consolini, Davide Buratti","doi":"10.1109/ETFA.2017.8247694","DOIUrl":null,"url":null,"abstract":"In industrial weight-filling machines, containers are filled with the liquid stored in a tank by an electronically controlled valve. The weight is sensed through a load cell. We develop a learning algorithm that predicts the right closure time as a function of liquid pressure and temperature. The algorithm solves a non-convex robust regression problem and is based on a branch and bound approach in regressors space.","PeriodicalId":6522,"journal":{"name":"2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)","volume":"27 1","pages":"1-6"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Robust regression for adaptive control of industrial weight fillers\",\"authors\":\"F. Denaro, L. Consolini, Davide Buratti\",\"doi\":\"10.1109/ETFA.2017.8247694\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In industrial weight-filling machines, containers are filled with the liquid stored in a tank by an electronically controlled valve. The weight is sensed through a load cell. We develop a learning algorithm that predicts the right closure time as a function of liquid pressure and temperature. The algorithm solves a non-convex robust regression problem and is based on a branch and bound approach in regressors space.\",\"PeriodicalId\":6522,\"journal\":{\"name\":\"2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)\",\"volume\":\"27 1\",\"pages\":\"1-6\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ETFA.2017.8247694\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ETFA.2017.8247694","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Robust regression for adaptive control of industrial weight fillers
In industrial weight-filling machines, containers are filled with the liquid stored in a tank by an electronically controlled valve. The weight is sensed through a load cell. We develop a learning algorithm that predicts the right closure time as a function of liquid pressure and temperature. The algorithm solves a non-convex robust regression problem and is based on a branch and bound approach in regressors space.
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