{"title":"用非参数算法对具有未知输入的离散系统进行滤波和预测","authors":"G. Koshkin, V. Smagin","doi":"10.1109/DT.2014.6868702","DOIUrl":null,"url":null,"abstract":"The paper addressed the filtering and prediction problems with using nonparametric algorithms for discrete stochastic systems with unknown input. The designed algorithms are based on combining the Kalman filter and nonparametric estimator. The optimal properties of the explored algorithms are proved. Examples are given to illustrate the usefulness of the proposed approach.","PeriodicalId":330975,"journal":{"name":"The 10th International Conference on Digital Technologies 2014","volume":"113 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Filtering and prediction for discrete systems with unknown input using nonparametric algorithms\",\"authors\":\"G. Koshkin, V. Smagin\",\"doi\":\"10.1109/DT.2014.6868702\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper addressed the filtering and prediction problems with using nonparametric algorithms for discrete stochastic systems with unknown input. The designed algorithms are based on combining the Kalman filter and nonparametric estimator. The optimal properties of the explored algorithms are proved. Examples are given to illustrate the usefulness of the proposed approach.\",\"PeriodicalId\":330975,\"journal\":{\"name\":\"The 10th International Conference on Digital Technologies 2014\",\"volume\":\"113 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-07-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The 10th International Conference on Digital Technologies 2014\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DT.2014.6868702\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 10th International Conference on Digital Technologies 2014","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DT.2014.6868702","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Filtering and prediction for discrete systems with unknown input using nonparametric algorithms
The paper addressed the filtering and prediction problems with using nonparametric algorithms for discrete stochastic systems with unknown input. The designed algorithms are based on combining the Kalman filter and nonparametric estimator. The optimal properties of the explored algorithms are proved. Examples are given to illustrate the usefulness of the proposed approach.
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