{"title":"模糊决策空间中的k步预测在预测中的应用","authors":"C. Frélicot, B. Dubuisson","doi":"10.1109/FUZZY.1992.258806","DOIUrl":null,"url":null,"abstract":"The authors demonstrate the ability and the accuracy of a modified extended Kalman filter used as a k-step-ahead predictor to perform a predicted membership function's point in a fuzzy decision space based on fuzzy pattern recognition principles, instead of a predicted state in the feature space. Results obtained with this prediction procedure are presented. A scheme including both fuzzy decision and prediction procedures is proposed for prognosis.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"69 8","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"K-step ahead prediction in fuzzy decision space-application to prognosis\",\"authors\":\"C. Frélicot, B. Dubuisson\",\"doi\":\"10.1109/FUZZY.1992.258806\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The authors demonstrate the ability and the accuracy of a modified extended Kalman filter used as a k-step-ahead predictor to perform a predicted membership function's point in a fuzzy decision space based on fuzzy pattern recognition principles, instead of a predicted state in the feature space. Results obtained with this prediction procedure are presented. A scheme including both fuzzy decision and prediction procedures is proposed for prognosis.<<ETX>>\",\"PeriodicalId\":222263,\"journal\":{\"name\":\"[1992 Proceedings] IEEE International Conference on Fuzzy Systems\",\"volume\":\"69 8\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1992-03-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[1992 Proceedings] IEEE International Conference on Fuzzy Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FUZZY.1992.258806\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FUZZY.1992.258806","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
K-step ahead prediction in fuzzy decision space-application to prognosis
The authors demonstrate the ability and the accuracy of a modified extended Kalman filter used as a k-step-ahead predictor to perform a predicted membership function's point in a fuzzy decision space based on fuzzy pattern recognition principles, instead of a predicted state in the feature space. Results obtained with this prediction procedure are presented. A scheme including both fuzzy decision and prediction procedures is proposed for prognosis.<>