{"title":"一种新的动态进程识别核方法","authors":"O. Taouali","doi":"10.1109/STA50679.2020.9329304","DOIUrl":null,"url":null,"abstract":"This paper proposes a new kernel method for dynamic process modelling. The developed algorithm is titled Adaptive Reduced Kernel Partial Least Squares (ARKPLS). The developed ARKPLS uses the Reduced KPLS technique in an offline scenario in order to build a model which have a small parameter number after that, the number of the retained parameters are update in an online scenario. The suggested technique has been used to identify a nonlinear process.","PeriodicalId":158545,"journal":{"name":"2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)","volume":"2021 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Novel Kernel method for Dynamic Process Identification\",\"authors\":\"O. Taouali\",\"doi\":\"10.1109/STA50679.2020.9329304\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a new kernel method for dynamic process modelling. The developed algorithm is titled Adaptive Reduced Kernel Partial Least Squares (ARKPLS). The developed ARKPLS uses the Reduced KPLS technique in an offline scenario in order to build a model which have a small parameter number after that, the number of the retained parameters are update in an online scenario. The suggested technique has been used to identify a nonlinear process.\",\"PeriodicalId\":158545,\"journal\":{\"name\":\"2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)\",\"volume\":\"2021 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/STA50679.2020.9329304\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/STA50679.2020.9329304","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Novel Kernel method for Dynamic Process Identification
This paper proposes a new kernel method for dynamic process modelling. The developed algorithm is titled Adaptive Reduced Kernel Partial Least Squares (ARKPLS). The developed ARKPLS uses the Reduced KPLS technique in an offline scenario in order to build a model which have a small parameter number after that, the number of the retained parameters are update in an online scenario. The suggested technique has been used to identify a nonlinear process.