{"title":"基于双支持向量机回归的ARX Hammerstein模型辨识","authors":"M. Aldhaifallah, K. Nisar","doi":"10.1109/SSD.2016.7473657","DOIUrl":null,"url":null,"abstract":"In this paper we develop a new algorithm to identify Auto-Regressive Exogenous (ARX) input Hammerstein Models based on Twin Support Vector Machine Regression (TSVR). The model is determined by minimizing two ε-insensitive loss functions. One of them determines the ε1-insensitive down bound regressor while the other determines the ε1-insensitive up bound regressor. The algorithm is compared to Support Vector Machine (SVM) and Least Square Support Vector Machine (LSSVM) based algorithms using simulation.","PeriodicalId":149580,"journal":{"name":"2016 13th International Multi-Conference on Systems, Signals & Devices (SSD)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identification of ARX Hammerstein Models based on Twin Support Vector Machine Regression\",\"authors\":\"M. Aldhaifallah, K. Nisar\",\"doi\":\"10.1109/SSD.2016.7473657\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we develop a new algorithm to identify Auto-Regressive Exogenous (ARX) input Hammerstein Models based on Twin Support Vector Machine Regression (TSVR). The model is determined by minimizing two ε-insensitive loss functions. One of them determines the ε1-insensitive down bound regressor while the other determines the ε1-insensitive up bound regressor. The algorithm is compared to Support Vector Machine (SVM) and Least Square Support Vector Machine (LSSVM) based algorithms using simulation.\",\"PeriodicalId\":149580,\"journal\":{\"name\":\"2016 13th International Multi-Conference on Systems, Signals & Devices (SSD)\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-03-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 13th International Multi-Conference on Systems, Signals & Devices (SSD)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SSD.2016.7473657\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 13th International Multi-Conference on Systems, Signals & Devices (SSD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSD.2016.7473657","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Identification of ARX Hammerstein Models based on Twin Support Vector Machine Regression
In this paper we develop a new algorithm to identify Auto-Regressive Exogenous (ARX) input Hammerstein Models based on Twin Support Vector Machine Regression (TSVR). The model is determined by minimizing two ε-insensitive loss functions. One of them determines the ε1-insensitive down bound regressor while the other determines the ε1-insensitive up bound regressor. The algorithm is compared to Support Vector Machine (SVM) and Least Square Support Vector Machine (LSSVM) based algorithms using simulation.