標題: General fuzzy piecewise regression analysis with automatic change-point detection 作者: Yu, JRTzeng, GHLi, HL管理學院運輸與物流管理系 註：原交通所+運管所College of ManagementDepartment of Transportation and Logistics Management 關鍵字: fuzzy regression;piecewise regression;change-point;possibility;necessity 公開日期: 16-四月-2001 摘要: Yu et al. (Fuzzy Sets and Systems 105 (1999) 429) performed general piecewise necessity regression analysis based on linear programming (LP) to obtain the necessity area. Their method is the same as that according to data distribution, even if the data are irregular, practitioners must specify the number and the positions of change-points. However, as the sample size increases, the number of change-points increases and the piecewise linear interval model also becomes complex. Therefore, this work devises general fuzzy piecewise regression analysis with automatic change-point detection to simultaneously obtain the fuzzy regression model and the positions of change-points. Fuzzy piecewise possibility and necessity regression models are employed when the function behaves differently in different parts of the range of crisp input variables. As stated, the above problem can be formulated as a mixed-integer programming problem. The proposed fuzzy piecewise regression method has three advantages: (a) Previously specifying the number of change-points, then the positions of change-points and the fuzzy piecewise regression model are obtained simultaneously. (b) It is more robust than conventional fuzzy regression. The conventional regression is sensitive to outliers. In contrast, utilizing piecewise concept, the proposed method can deal with outliers by automatically segmenting the data. (c) By employing the mixed integer programming, the solution is the global optimal rather than local optimal solution. For illustrating more detail, two numerical examples are shown in this paper. By using the proposed method, the fuzzy piecewise regression model with detecting change-points can be derived simultaneously. (C) 2001 Elsevier Science B.V. All rights reserved. URI: http://dx.doi.org/10.1016/S0165-0114(98)00384-4http://hdl.handle.net/11536/29705 ISSN: 0165-0114 DOI: 10.1016/S0165-0114(98)00384-4 期刊: FUZZY SETS AND SYSTEMS Volume: 119 Issue: 2 起始頁: 247 結束頁: 257 顯示於類別： 期刊論文