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A MODIFICATION ON RIDGE ESTIMATION FOR FUZZY NONPARAMETRIC REGRESSION | ||
| Iranian Journal of Fuzzy Systems | ||
| مقاله 7، دوره 9، شماره 2، شهریور 2012، صفحه 75-88 اصل مقاله (257.19 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22111/ijfs.2012.208 | ||
| نویسندگان | ||
Rahman Farnoosh  1؛ Javad Ghasemian2؛ Omid Solaymani Fard3
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| 1School of Mathematics, Iran University of Science and Tech- nology, Narmak, Tehran-16846, Iran | ||
| 2School of Mathematics, Iran University of Science and Technol- ogy, Narmak, Tehran-16846, Iran | ||
| 3School of Mathematics and Computer Science, Damghan Uni- versity, Damghan, Iran | ||
| چکیده | ||
| This paper deals with ridge estimation of fuzzy nonparametric regression models using triangular fuzzy numbers. This estimation method is obtained by implementing ridge regression learning algorithm in the La- grangian dual space. The distance measure for fuzzy numbers that suggested by Diamond is used and the local linear smoothing technique with the cross- validation procedure for selecting the optimal value of the smoothing param- eter is fuzzi ed to t the presented model. Some simulation experiments are then presented which indicate the performance of the proposed method. | ||
| کلیدواژه‌ها | ||
| Fuzzy regression؛ Ridge estimation؛ Fuzzy nonparametric regression؛ Local linear smoothing | ||
| مراجع | ||
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[1] C. B. Cheng and E. S. Lee, Fuzzy regression with radial basis function networks, Fuzzy Sets and Systems, 119 (2001), 291-301. [2] P. Diamond, Fuzzy least squares, Information Sciences, 46 (1988), 141-157. [3] N. R. Draper and H. Smith, Applied Regression Analysis, Wiley, New York, 1980. [4] H. Drucker, C. Burges, L. Kaufman, A. Smola and V. N. Vapnik, Support vector regression machines, in: M. C. Mozer, M. I. Jordan, T. Petsche, Eds., Advances in Neural Information Processing Systems, MIT Press, Cambridge, MA, 9 (1996), 155-162. [5] O. S. Fard and A. V. Kamyad, Modi ed k-step method for solving fuzzy initial value problems, Iranian Journal of Fuzzy Systems, 8(1) (2011), 49-63. [6] J. Fan and I. Gijbels, Local Polynomial Modeling and Its Applications, Chapman & Hall, London, 1996. [7] W. Hardle, Applied Nonparametric Regression, Cambridge University Press, New York, 1990. [8] J. D. Hart, Nonparametric Smoothing and Lack-of- t Tests, Springer-Verlag, New York, 1997. [9] T. J. Hastie and R. J. Tibshirani, Generalized Additive Models, Chapman & Hall, London, 1990. [10] D. H. Hong and C. Hwang, Support vector fuzzy regression machines, Fuzzy Sets and Systems, 138 (2003), 271-281. [11] D. H. Hong, C. Hwang and C. Ahn, Ridge estimation for regression models with crisp inputs and Gaussian fuzzy output, Fuzzy Sets and Systems, 142 (2004), 307-319. [12] A. E. Hoerl and R. W. Kennard, Ridge regression: biased estimates for nonorthogonal prob- lems, Technometrics, 12 (1970), 55-67. [13] H. Ishibuchi and H. Tanaka, Fuzzy regression analysis using neural networks, Fuzzy Sets and Systems, 50 (1992), 257-265. [14] H. Ishibuchi and H. Tanaka, Fuzzy neural networks with interval weights and its application to fuzzy regression analysis, Fuzzy Sets and Systems, 57 (1993), 27-39. [15] B. Kim and R. R. Bishu, Evaluation of fuzzy linear regression models by comparing mem- bership functions, Fuzzy Sets and Systems, 100 (1998), 343-352. [16] R. X. Liu, J. Kuang, Q. Gong and X. L. Hou, Principal component regression analysis with SPSS, Computer Methods and Programs in Biomedicine, 71 (2003), 141-147. [17] S. Pourahmad, S. M. T. Ayatollahi and S. M. Taheri, Fuzzy logistic regression: a new possi- bilistic model and its application in clinical vague status, Iranian Journal of Fuzzy Systems, 8 (2011), 1-17. [18] H. Shakouri G and R. Nadimi, A novel fuzzy linear regression model based on a non-equality possibility index and optimum uncertainty, Applied Soft Computing, 9 (2009), 590-598. [19] C. Saunders, A. Gammerman and V. Vork, Ridge regression learning algorithm in dual vari- able, Proceedings of the 15th International Conference on Machine Learning, (1998), 515-521. [20] N. Wang, W. X. Zhang and C. L. Mei, Fuzzy nonparametric regression based on local linear smoothing technique, Information Sciences, 177 (2007), 3882-3900. [21] M. S. Yang and C. H. Ko, On a class of fuzzy c-numbers clustering procedures for fuzzy data, Fuzzy Sets and Systems, 84 (1996), 49-60. | ||
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