C. B. Cheng and E. S. Lee, Fuzzy regression with radial basis function networks, Fuzzy Sets
and Systems, 119 (2001), 291-301.
 P. Diamond, Fuzzy least squares, Information Sciences, 46 (1988), 141-157.
 N. R. Draper and H. Smith, Applied Regression Analysis, Wiley, New York, 1980.
 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.
 O. S. Fard and A. V. Kamyad, Modied k-step method for solving fuzzy initial value problems,
Iranian Journal of Fuzzy Systems, 8(1) (2011), 49-63.
 J. Fan and I. Gijbels, Local Polynomial Modeling and Its Applications, Chapman & Hall,
 W. Hardle, Applied Nonparametric Regression, Cambridge University Press, New York, 1990.
 J. D. Hart, Nonparametric Smoothing and Lack-of-t Tests, Springer-Verlag, New York, 1997.
 T. J. Hastie and R. J. Tibshirani, Generalized Additive Models, Chapman & Hall, London,
 D. H. Hong and C. Hwang, Support vector fuzzy regression machines, Fuzzy Sets and Systems,
138 (2003), 271-281.
 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.
 A. E. Hoerl and R. W. Kennard, Ridge regression: biased estimates for nonorthogonal prob-
lems, Technometrics, 12 (1970), 55-67.
 H. Ishibuchi and H. Tanaka, Fuzzy regression analysis using neural networks, Fuzzy Sets and
Systems, 50 (1992), 257-265.
 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.
 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.
 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.
 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.
 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.
 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.
 N. Wang, W. X. Zhang and C. L. Mei, Fuzzy nonparametric regression based on local linear
smoothing technique, Information Sciences, 177 (2007), 3882-3900.
 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.