اخبار و اعلانات

 آمار

تعداد نشریات31
تعداد شماره‌ها834
تعداد مقالات8,021
تعداد مشاهده مقاله14,857,890
تعداد دریافت فایل اصل مقاله9,599,250
Hassanpour, H., Malek, H. R., Yaghoobi, M. A.. (1388). A NOTE ON EVALUATION OF FUZZY LINEAR REGRESSION MODELS BY COMPARING MEMBERSHIP FUNCTIONS. نشریه علمی دانشگاه سیستان و بلوچستان, 6(2), 1-6. doi: 10.22111/ijfs.2009.203
H. Hassanpour; H. R. Malek; M. A. Yaghoobi. "A NOTE ON EVALUATION OF FUZZY LINEAR REGRESSION MODELS BY COMPARING MEMBERSHIP FUNCTIONS". نشریه علمی دانشگاه سیستان و بلوچستان, 6, 2, 1388, 1-6. doi: 10.22111/ijfs.2009.203
Hassanpour, H., Malek, H. R., Yaghoobi, M. A.. (1388). 'A NOTE ON EVALUATION OF FUZZY LINEAR REGRESSION MODELS BY COMPARING MEMBERSHIP FUNCTIONS', نشریه علمی دانشگاه سیستان و بلوچستان, 6(2), pp. 1-6. doi: 10.22111/ijfs.2009.203
Hassanpour, H., Malek, H. R., Yaghoobi, M. A.. A NOTE ON EVALUATION OF FUZZY LINEAR REGRESSION MODELS BY COMPARING MEMBERSHIP FUNCTIONS. نشریه علمی دانشگاه سیستان و بلوچستان, 1388; 6(2): 1-6. doi: 10.22111/ijfs.2009.203

A NOTE ON EVALUATION OF FUZZY LINEAR REGRESSION MODELS BY COMPARING MEMBERSHIP FUNCTIONS

Iranian Journal of Fuzzy Systems
مقاله 2، دوره 6، شماره 2، شهریور 2009، صفحه 1-6    اصل مقاله (143.52 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22111/ijfs.2009.203
نویسندگان
H. Hassanpour1؛ H. R. Malek* 2؛ M. A. Yaghoobi3
1Department of Mathematics, University of Birjand, Birjand, Iran
2Faculty of Basic Sciences, Shiraz University of Technology, Shiraz, Iran
3Department of Statistics, Shahid Bahonar University of Kerman, Kerman, Iran
چکیده
Kim and Bishu (Fuzzy Sets and Systems 100 (1998) 343-352) proposed
a modification of fuzzy linear regression analysis. Their modification
is based on a criterion of minimizing the difference of the fuzzy membership
values between the observed and estimated fuzzy numbers. We show that their
method often does not find acceptable fuzzy linear regression coefficients and
to overcome this shortcoming, propose a modification. Finally, we present two
numerical examples to illustrate efficiency of the modified method.
کلیدواژه‌ها
Fuzzy linear regression؛ Fuzzy number؛ Least-squares method. This paper is supported in part by Fuzzy Systems and Applications Center of Excellence؛ Shahid Bahonar University of Kerman؛ Kerman؛ I.R. of Iran
مراجع
[1] A. Abdalla and J. J. Buckley, Monte Carlo methods in fuzzy linear regression, Soft Computing,
11 (2007), 991-996.
[2] S. Abbasbandy and M. Alavi, A method for solving fuzzy linear systems, Iranian Journal of
Fuzzy Systems, 2(2) (2005), 37-44.
[3] S. M. Abu Nayeem and M. Pal, The p-center problem on fuzzy networks and reduction of
cost, Iranian Journal of Fuzzy Systems, 5(1) (2008), 1-26.
[4] A. R. Arabpour and M. Tata, Estimating the parameters of a fuzzy linear regression model,
Iranian Journal of Fuzzy Systems, 5(2) (2008), 1-19.
[5] J. M. Cadenas and J. L. Verdegay, A primer on fuzzy optimization models and methods,
Iranian Journal of Fuzzy Systems, 3(1) (2006), 1-22.
[6] S. H. Choi and J. J. Buckley, Fuzzy regression using least absolute deviation estimators, Soft
Computing, 12(3) (2008), 257-263.
[7] C. Kao and C. L. Chyu, A fuzzy linear regression model with better explanatory power, Fuzzy
Sets and Systems, 126 (2002), 401-409.

[8] C. Kao and C. L. Chyu, Least-squares estimates in fuzzy regression analysis, European Journal
of Operational Research, 148 (2003), 426-435.
[9] B. Kim and R. R. Bishu, Evaluation of fuzzy linear regression models by comparing membership
functions, Fuzzy Sets and Systems, 100 (1998), 343-352.
[10] H. Lee and H. Tanaka, Upper and lower approximation models in interval regression using
regression quantile techniques, European Journal of Operational Research, 116 (1999), 653-
666.
[11] MATLAB 7.0, The Mathworks, (www.mathworks.com).
[12] M. Modarres, E. Nasrabadi and M. M. Nasrabadi, Fuzzy linear regression models with least
square errors, Applied Mathematics and Computation, 163 (2005), 977-989.
[13] J. Mohammadi and S. M. Taheri, Pedomodels fitting with fuzzy least squares regression,
Iranian Journal of Fuzzy Systems, 1(29) (2004), 45-62.
[14] M. Sakawa and H. Yano, Fuzzy linear regression and its applications, In: J. Kacprzyk and
M. Fedrizzi (Eds.), Studies in fuzziness, fuzzy regression analysis, Omnitech Press, Warsaw,
Poland, (1992), 61-80.
[15] M. R. Safi, H. R. Maleki and E. Zaeimazad, A note on Zimmermann method for solving
fuzzy linear programming problems, Iranian Journal of Fuzzy Systems, 4(2) (2007), 31-46.
[16] E. Shivanian, E. Khorram and A. Ghodousian, Optimization of linear objective function subject
to fuzzy relation inequalities constraints with Max-average composition, Iranian Journal
of Fuzzy Systems, 4(2) (2007), 15-30.
[17] S. M. Vaezpour and F. Karimi, t-Best approximation in fuzzy normed spaces, Iranian Journal
of Fuzzy Systems, 5(2) (2008), 93-99.
[18] H. C. Wu, Fuzzy linear regression model based on fuzzy scalar product, Soft Computing, 12
(2008), 469-477.
[19] M. A. Yaghoobi and M. Tamiz, A Short note on the relationship between goal programming
and fuzzy programming for vectormaximum problems, Iranian Journal of Fuzzy Systems, 2
(2) (2005), 31-36.
[20] L. A. Zadeh, Fuzzy sets, Information and Control, 8 (1965), 338-353.

آمار
تعداد مشاهده مقاله: 2,743
تعداد دریافت فایل اصل مقاله: 2,653


سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب