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研究生:余菁蓉
研究生(外文):Yu Jing Rung
論文名稱:偵測改變點之模糊逐段迴歸模式
論文名稱(外文):A Fuzzy Piecewise Regression Model with Change-point Detection
指導教授:黎漢林黎漢林引用關係
指導教授(外文):Li Han Lin
學位類別:博士
校院名稱:國立交通大學
系所名稱:資訊管理所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:1999
畢業學年度:87
語文別:英文
論文頁數:61
中文關鍵詞:模糊迴歸逐段二次規劃可能性必然性
外文關鍵詞:Fuzzy RegressionPiecewiseQuadratic programmingPossibilityNecessity
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Tanaka and Ishibuchi 所提出的模糊迴歸分析法當資料變異非常大時,可能性模式所構成的區間很寬而必然性模式則無法算出;此外,他們的方法所求出的模式,其係數常不是模糊數。為處理資料變異大的問題,本文提一模糊逐段迴歸模式,並且也採用二次規劃來處理模糊數寬度為零的現象。本研究所提出模糊逐段迴歸模式有兩個優點:一、可同時算出模糊逐段迴歸模式和改變點位置;二、可透過自動區隔資料偵測到離群值。
The possibilistic regression analysis proposed by Tanaka and Ishibuchi, which is extremely sensitive to outliers, may not able to find feasible solution. Besides, when they use linear programming in possibilistic regression analysis, some coefficients are limited to be crisp because of the characteristic of linear programming. To overcome large variation problem, we propose fuzzy piecewise regression method. Our method can also treat the problem with crisp coefficients by utilizing quadratic programming approach. The proposed fuzzy piecewise regression method has two advantages: (a) It can detect the positions of change-points and can estimate the fuzzy piecewise regression model simultaneously; (b) It can deal with outliers by automatically segmenting the data.
封面
Figures
Tables
摘要
Abstract
Chapter 1 Introduction
1.1 Research Background
1.2 Research motivation and purposes
1.3 Organization
Chapter 2 Basic concepts of possibility and necessity regression models
2.1 Interval arithmetic
2.2 Possibility regression analysis
2.3 Necessity regression analysis
2.4 Example and discussions
Chapter 3 Interval regression model by quadratic programming approach
3.1 Interval arithmetic for unifying possibility and necessity analysis
3.2 Quadratic programming approach unifying the possibility and necessity models
3.3 Example and discussions
Chapter 4 Necessity regression model with fuzzy piecewise regression
4.1 Fuzzy piecewise regression analysis
4.2 Multiariate piecewise linear regression in the necessity problems
4.3 Numerical examples
4.4 Discussions
Chapter 5 Fuzzy piecewise regression with automatic change-point detection
5.1 Possibility analysis with automatic change-point detection
5.2 Necessity analysis with automatic change-point detection
5.3 Numerical examples
5.4 Discussions
Chapter 6 Automatic change-point detection by quadratic programming approach
6.1 The unified quadratic programming
6.2 Numerical example
6.3 Discussions
Chapter 7 Conclusions and remarks
References
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