{"title":"基于区间系数样条模糊模型的非线性区间回归分析","authors":"Lili Cai, Degang Wang, Wenyan Song, Hongxing Li","doi":"10.1109/ICICIP.2016.7885903","DOIUrl":null,"url":null,"abstract":"Interval regression analysis is an effective method of handling uncertain and imprecise data. In this paper, a kind of spline fuzzy model is designed for modeling interval data. Spline function is chosen as the membership function, and the coefficients of this model are taken as interval-valued numbers. A target function based on approximation errors and specificity is proposed to improve the quality of estimated interval data. Accordingly, gradient descent algorithm is employed to tune these interval weights. Some numerical simulations are carried out to validate the effectiveness of the proposed spline interval regression model.","PeriodicalId":226381,"journal":{"name":"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Nonlinear interval regression analysis based on spline fuzzy model with interval coefficients\",\"authors\":\"Lili Cai, Degang Wang, Wenyan Song, Hongxing Li\",\"doi\":\"10.1109/ICICIP.2016.7885903\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Interval regression analysis is an effective method of handling uncertain and imprecise data. In this paper, a kind of spline fuzzy model is designed for modeling interval data. Spline function is chosen as the membership function, and the coefficients of this model are taken as interval-valued numbers. A target function based on approximation errors and specificity is proposed to improve the quality of estimated interval data. Accordingly, gradient descent algorithm is employed to tune these interval weights. Some numerical simulations are carried out to validate the effectiveness of the proposed spline interval regression model.\",\"PeriodicalId\":226381,\"journal\":{\"name\":\"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICICIP.2016.7885903\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICIP.2016.7885903","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Nonlinear interval regression analysis based on spline fuzzy model with interval coefficients
Interval regression analysis is an effective method of handling uncertain and imprecise data. In this paper, a kind of spline fuzzy model is designed for modeling interval data. Spline function is chosen as the membership function, and the coefficients of this model are taken as interval-valued numbers. A target function based on approximation errors and specificity is proposed to improve the quality of estimated interval data. Accordingly, gradient descent algorithm is employed to tune these interval weights. Some numerical simulations are carried out to validate the effectiveness of the proposed spline interval regression model.
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