{"title":"依赖观测下的强一致递归回归估计","authors":"K. Chernyshov","doi":"10.1109/ISCAS.2000.857381","DOIUrl":null,"url":null,"abstract":"The paper is focused on establishing strong consistency of recursive estimates of nonlinear characteristics of dynamic systems. To describe the shape of the nonlinearities, the regression function kernel type estimates are used. Within the approach presented, a feature of the technique is considering a case of mutually dependent observations. Simultaneously, only mild and easy verified assumptions with respect to the system's input and output processes, as well as to the external disturbances, are involved.","PeriodicalId":6422,"journal":{"name":"2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353)","volume":"46 1","pages":"133-136 vol.5"},"PeriodicalIF":0.0000,"publicationDate":"2000-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Strongly consistent recursive regression estimation under depended observations\",\"authors\":\"K. Chernyshov\",\"doi\":\"10.1109/ISCAS.2000.857381\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper is focused on establishing strong consistency of recursive estimates of nonlinear characteristics of dynamic systems. To describe the shape of the nonlinearities, the regression function kernel type estimates are used. Within the approach presented, a feature of the technique is considering a case of mutually dependent observations. Simultaneously, only mild and easy verified assumptions with respect to the system's input and output processes, as well as to the external disturbances, are involved.\",\"PeriodicalId\":6422,\"journal\":{\"name\":\"2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353)\",\"volume\":\"46 1\",\"pages\":\"133-136 vol.5\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2000-05-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCAS.2000.857381\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCAS.2000.857381","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Strongly consistent recursive regression estimation under depended observations
The paper is focused on establishing strong consistency of recursive estimates of nonlinear characteristics of dynamic systems. To describe the shape of the nonlinearities, the regression function kernel type estimates are used. Within the approach presented, a feature of the technique is considering a case of mutually dependent observations. Simultaneously, only mild and easy verified assumptions with respect to the system's input and output processes, as well as to the external disturbances, are involved.
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