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題名:偵測改變點之模糊逐段迴歸模式
作者:余菁蓉
作者(外文):Yu Jing Rung
校院名稱:國立交通大學
系所名稱:資訊管理所
指導教授:黎漢林
學位類別:博士
出版日期:1999
主題關鍵詞:模糊迴歸逐段二次規劃可能性必然性Fuzzy RegressionPiecewiseQuadratic programmingPossibilityNecessity
原始連結:連回原系統網址new window
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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.
G. Alefeld and J. Herzberger (1983), Introduction to Interval Computations,
Academic Press, New York.
M. J. Best (1984), Equivalence of some quadratic programming algorithms, Math.
Programming, 30, 71-87.
P. T. Chang, E. S. Lee and S. A. Konz (1996), Applying fuzzy linear regression to
VDT legibility, Fuzzy Sets and Systems, 80, 197-204.
A. Celmins (1987a), Least squares model fitting to fuzzy vector data, Fuzzy Sets and Systems, 22, 245-269.
A. Celmins (1987b), Multidimensional least squares fitting of fuzzy model, Math. Modeling, 9, 669-690.
P. Diamond (1988), Fuzzy least squares, Information Science, 46, 141-157.
D. Dubois and H. Prade (1988), Possibility theory, Prenum Press, New York.
P. E. Gill and W. Murray (1978), Numerically stable methods for quadratic
programming, Math. Programming, 14, 349-372.
D. Goldfarb and A. Indani (1983), A numerically stable dual method for solving
strictly convex quadratic programs, Math. Programming, 27, 1-33.
M. Inuiguchi, N. Sakawa and S. Ushiro (1993), Interval regression based on
Minkowski''s subtraction, Fifth IFSA World Congress, Soul, Korea, 505-508.
M. Kaneyoshi, H. Tanaka, M. Kamei and H. Furuta (1990), New system identification
technique using fuzzy regression analysis, Proceedings of First Int. Sym. On
Uncertainty Modeling and Analysis, Maryland, USA, 528-533.
Kwang Jae Kim, Herbert Moskowitz and Murat Koksalan (1996), Fuzzy versus
statistical regression, European Journal of Operational Research, 92, 417-434.
H. L. Li and J. R.Yu (1999), A general piecewise regression with automatic change-
point detection, Intelligent Data Analysis, 3, 75-85.
LINDO Systems Inc. (1998), Optimization Modeling with LINGO, Chicago.
D. C. Montgomery (1992), Introduction to Linear Regression Analysis, Wiely, New
York.
H. Moskowitz and K. Kim (1993), On accessing the H value in fuzzy linear
regression, Fuzzy Sets and Systems, 58, 303-327.
Georg Peters (1994), Fuzzy linear regression with fuzzy intervals, Fuzzy Sets and
Systems, 63, 45-55.
Z. Pawlak (1984), Rough classification, International Journal of Man-Machine
Studies, 20, 469-483.
Z. Pawlak (1991), Rough sets: Theoretical Aspects of Reasoning about Data, MA:
Kluwer, Boston.
D. T. Redden and W. H. Woodal (1994), Properties of certain fuzzy linear regression
methods, Fuzzy Sets and Systems, 64, 361-375.
D. T. Redden and W. H. Woodal (1996), Further examination of fuzzy linear
regression, Fuzzy Sets and Systems, 79, 203-211.
M. Sakawa and H. Yano (1992), Multiobjective fuzzy linear regression analysis for
fuzzy input-output data, Fuzzy Sets and Systems, 46, 173-181.
M. Sakawa and H. Yano (1992), Fuzzy linear regression and its application, in: J.
Kacprzyk and M. Fedrizzi, Eds., Studies in Fuzziness, Fuzzy Regression Analysis, Vol. 1, 61-80, Omnitech Press, Warsaw, Poland.new window
G. A. F. Seber and C. J. Wild (1989), Nonlinear Regression, Wiely, New York.
Slowinski R. (Eds.) (1998), Fuzzy stes in decision analysis, operations research and
Statistics, 349-387, Kluwer Academic Publishers, Massachusetts.
H. Tanaka (1987), Fuzzy data analysis by possibilistic linear models, Fuzzy Sets and
Systems, 28, 363-375.
H. Tanaka, I. Hayashi and J. Watada (1989), Possibilistic linear regression analysis
for fuzzy data, European Journal of Operational Research, 40, 389-396.
H. Tanaka and H. Ishibuchi (1992), Possibilistic regression analysis based on linear
programming, in: J. Kacprzyk and M. Fedrizzi, Eds., Studies in Fuzziness, Fuzzy
Regression Analysis, Vol. 1, 47-60, Omnitech Press, Warsaw Poland.new window
H. Tanaka and H. Ishibuchi (1991), Identification of possibilistic linear quadratic
membership functions, Fuzzy Sets and Systems, 41, 145-160.
H. Tanaka, H. Ishibuchi and S. Yoshikawa (1995), Exponential possibility regression
analysis, Fuzzy Sets and Systems, 69, 305-318.
H. Tanaka and H. Lee (1998), Interval Regression Analysis by Quadratic
Programming Approach, IEEE Transactions on Fuzzy Systems, 6, 473-481.
H. Tanaka, S. Uejima, and K. Asai (1982), Linear regression analysis with fuzzy
regression model, IEEE Transactions on Systems, Man, and Cybernetics, 12
903-907.
H. Tanaka and J. Watada (1988), Possibilistic linear systems and their application to
the linear regression model, Fuzzy Sets and Systems, 27, 275-289.
J. R. Yu, G. H. Tzeng and H. L. Li, (1999) A general piecewise necessity regression
analysis based on linear programming, Fuzzy Sets and Systems, 105, 429-436.
J. R. Yu, Gwo-Hshiung Tzeng and Han-Lin Li (1999), General Fuzzy Piecewise
Regression Analysis with Automatic Change-point Detection, Fuzzy Sets and
Systems (forthcoming).
L. A. Zadeh (1977), Fuzzy sets as basis for a theory of possibility, Fuzzy Sets and
Systems, 27, 3-28.
 
 
 
 
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