{"title":"Volterra系统辨识中高斯信号的正交化","authors":"V. J. Mathews","doi":"10.1109/DSP.1994.379863","DOIUrl":null,"url":null,"abstract":"This paper presents a very simple method for orthogonalizing Gaussian input signals for identification of truncated Volterra systems. The orthogonalization requires a linear lattice predictor and some nonlinear processing of the backward prediction error signals of the various orders. However, the nonlinear processors do not depend on the statistics of the input signals, and consequently, are easy to design and implement.<<ETX>>","PeriodicalId":189083,"journal":{"name":"Proceedings of IEEE 6th Digital Signal Processing Workshop","volume":"186 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Orthogonalization of Gaussian signals for Volterra system identification\",\"authors\":\"V. J. Mathews\",\"doi\":\"10.1109/DSP.1994.379863\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a very simple method for orthogonalizing Gaussian input signals for identification of truncated Volterra systems. The orthogonalization requires a linear lattice predictor and some nonlinear processing of the backward prediction error signals of the various orders. However, the nonlinear processors do not depend on the statistics of the input signals, and consequently, are easy to design and implement.<<ETX>>\",\"PeriodicalId\":189083,\"journal\":{\"name\":\"Proceedings of IEEE 6th Digital Signal Processing Workshop\",\"volume\":\"186 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-10-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of IEEE 6th Digital Signal Processing Workshop\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DSP.1994.379863\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of IEEE 6th Digital Signal Processing Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DSP.1994.379863","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Orthogonalization of Gaussian signals for Volterra system identification
This paper presents a very simple method for orthogonalizing Gaussian input signals for identification of truncated Volterra systems. The orthogonalization requires a linear lattice predictor and some nonlinear processing of the backward prediction error signals of the various orders. However, the nonlinear processors do not depend on the statistics of the input signals, and consequently, are easy to design and implement.<>