{"title":"用一种新准则辨识再现核希尔伯特空间中非线性系统","authors":"N. Souilem, I. Elaissi, O. Taouali, M. Hassani","doi":"10.1109/iccvia.2015.7351900","DOIUrl":null,"url":null,"abstract":"This paper proposes a new algorithm to estimate the required number of parameters in the models developed in Reproducing Kernel Hilbert Space (RKHS). The proposed method considers models with growing complexities and calculates for each a given matrix, such that these matrices tend to singularity. The required number of parameters is given by verifying a criterion on the determinants of these matrices.","PeriodicalId":419122,"journal":{"name":"International Conference on Computer Vision and Image Analysis Applications","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identification of non linear system modeled in Reproducing Kernel Hilbert Space using a new criterion\",\"authors\":\"N. Souilem, I. Elaissi, O. Taouali, M. Hassani\",\"doi\":\"10.1109/iccvia.2015.7351900\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a new algorithm to estimate the required number of parameters in the models developed in Reproducing Kernel Hilbert Space (RKHS). The proposed method considers models with growing complexities and calculates for each a given matrix, such that these matrices tend to singularity. The required number of parameters is given by verifying a criterion on the determinants of these matrices.\",\"PeriodicalId\":419122,\"journal\":{\"name\":\"International Conference on Computer Vision and Image Analysis Applications\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Computer Vision and Image Analysis Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/iccvia.2015.7351900\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Computer Vision and Image Analysis Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/iccvia.2015.7351900","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Identification of non linear system modeled in Reproducing Kernel Hilbert Space using a new criterion
This paper proposes a new algorithm to estimate the required number of parameters in the models developed in Reproducing Kernel Hilbert Space (RKHS). The proposed method considers models with growing complexities and calculates for each a given matrix, such that these matrices tend to singularity. The required number of parameters is given by verifying a criterion on the determinants of these matrices.