{"title":"广义Hammerstein模型在非多项式输入上的推广","authors":"A. Novák, L. Simon, P. Lotton","doi":"10.1109/EUSIPCO.2016.7760202","DOIUrl":null,"url":null,"abstract":"The Generalized Hammerstein model has been successfully used during last few years in many physical applications to describe the behavior of a nonlinear system under test. The main advantage of such a nonlinear model is its capability to model efficiently nonlinear systems while keeping the computational cost low. On the other hand, this model can not predict complicated nonlinear behaviors such as hysteretic one. In this paper, we propose an extension of the Generalized Hammerstein model to a model with non polynomial nonlinear inputs that allows modeling more complicated nonlinear systems. A simulation provided in this paper shows a good agreement between the model and the hysteretic nonlinear system under test.","PeriodicalId":127068,"journal":{"name":"2016 24th European Signal Processing Conference (EUSIPCO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Extension of Generalized Hammerstein model to non-polynomial inputs\",\"authors\":\"A. Novák, L. Simon, P. Lotton\",\"doi\":\"10.1109/EUSIPCO.2016.7760202\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Generalized Hammerstein model has been successfully used during last few years in many physical applications to describe the behavior of a nonlinear system under test. The main advantage of such a nonlinear model is its capability to model efficiently nonlinear systems while keeping the computational cost low. On the other hand, this model can not predict complicated nonlinear behaviors such as hysteretic one. In this paper, we propose an extension of the Generalized Hammerstein model to a model with non polynomial nonlinear inputs that allows modeling more complicated nonlinear systems. A simulation provided in this paper shows a good agreement between the model and the hysteretic nonlinear system under test.\",\"PeriodicalId\":127068,\"journal\":{\"name\":\"2016 24th European Signal Processing Conference (EUSIPCO)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 24th European Signal Processing Conference (EUSIPCO)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EUSIPCO.2016.7760202\",\"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 24th European Signal Processing Conference (EUSIPCO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EUSIPCO.2016.7760202","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Extension of Generalized Hammerstein model to non-polynomial inputs
The Generalized Hammerstein model has been successfully used during last few years in many physical applications to describe the behavior of a nonlinear system under test. The main advantage of such a nonlinear model is its capability to model efficiently nonlinear systems while keeping the computational cost low. On the other hand, this model can not predict complicated nonlinear behaviors such as hysteretic one. In this paper, we propose an extension of the Generalized Hammerstein model to a model with non polynomial nonlinear inputs that allows modeling more complicated nonlinear systems. A simulation provided in this paper shows a good agreement between the model and the hysteretic nonlinear system under test.