{"title":"非可逆非线性Wiener系统的辨识","authors":"Jing Ren, Guoqi Li","doi":"10.1109/CISP.2015.7407878","DOIUrl":null,"url":null,"abstract":"In this paper, we develop a new method to identify Wiener systems. Unlike previous techniques for Winer system identification, our method allows the linear dynamic subsystem to be infinite-impulse response (IIR) and the nonlinear function to be non-differentiable, discontinuous and non-invertible. Two input sequences are designed to estimate the nonlinear function as well as the parameters in the linear system. The convergence analysis is also discussed and the performance of the proposed method is illustrated by simulations.","PeriodicalId":167631,"journal":{"name":"2015 8th International Congress on Image and Signal Processing (CISP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Identification of Wiener systems with non-invertible nonlinearity\",\"authors\":\"Jing Ren, Guoqi Li\",\"doi\":\"10.1109/CISP.2015.7407878\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we develop a new method to identify Wiener systems. Unlike previous techniques for Winer system identification, our method allows the linear dynamic subsystem to be infinite-impulse response (IIR) and the nonlinear function to be non-differentiable, discontinuous and non-invertible. Two input sequences are designed to estimate the nonlinear function as well as the parameters in the linear system. The convergence analysis is also discussed and the performance of the proposed method is illustrated by simulations.\",\"PeriodicalId\":167631,\"journal\":{\"name\":\"2015 8th International Congress on Image and Signal Processing (CISP)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 8th International Congress on Image and Signal Processing (CISP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CISP.2015.7407878\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 8th International Congress on Image and Signal Processing (CISP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CISP.2015.7407878","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Identification of Wiener systems with non-invertible nonlinearity
In this paper, we develop a new method to identify Wiener systems. Unlike previous techniques for Winer system identification, our method allows the linear dynamic subsystem to be infinite-impulse response (IIR) and the nonlinear function to be non-differentiable, discontinuous and non-invertible. Two input sequences are designed to estimate the nonlinear function as well as the parameters in the linear system. The convergence analysis is also discussed and the performance of the proposed method is illustrated by simulations.