1 |
A. Celmins, Least model fitting to fuzzy vector data, Fuzzy Sets and Systems 22 (1987) 245-269
DOI
ScienceOn
|
2 |
A. Celmins, Multidimensional least-squares fitting of fuzzy models, Math. Modelling 9 (1987) 669-690
DOI
ScienceOn
|
3 |
H. Tanaka, Fuzzy data analysis by possibilistic linear models, Fuzzy Sets and Systems 24 (1987) 363-375
DOI
ScienceOn
|
4 |
D. Dubois and H. Prade, Theory and Applications, Fuzzy Sets and Systems, Academic Press, New York, 1980
|
5 |
J. Buckley and T. Feuring, Linear and non-linear fuzzy regression: Evolutionary algorithm solutions, Fuzzy Sets and Systems 112 (2000) 381-394
DOI
ScienceOn
|
6 |
J. Buckley, T. Feuring and Y. Hayashi, Multivariate non-linear fuzzy regression: An evolutionary algorithm approach, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 7 (1999) 83-98
DOI
|
7 |
A. J. Smola and B. Scholkopf, A Tutorial on Support Vector Regression, NeuroCOLT2 Technical Report, NeuroCOLT, 1998
|
8 |
A. Celmins, A practical approach to nonlinear fuzzy regression, SLAM J. Sci. Stat. Comput., Vol. 12 No. 3 (1991) 521-546
DOI
|
9 |
P. Diamond, Fuzzy least squares, Inform. Sci. 46 (1988) 141-157
DOI
ScienceOn
|
10 |
B. Heshmaty, A. Kandel, Fuzzy linear regression and its applications to forecasting in uncertain environment, Fuzzy Sets and Systems 15 (1985) 159-191
DOI
ScienceOn
|
11 |
R. Fletcher, Practical Methods of Optimization, John Wiley and Sons, Inc., 2nd edition, 1987
|
12 |
H. Goldstein, Classical Mechanis, Addison-Wesley, Reading, MA, 1986
|
13 |
S. Gunn, Support Vector Machines for Classification and Regression, ISIS Technical Report, U. of Southampton, 1998
|
14 |
J. Kacprzyk and M. Fedrizzi, Fuzzy Regression Analysis(Physica-Verlag, Heidelberg, 1992)
|
15 |
G. P. McCormick, Nonlinear Programing: Theory, Algorithms and Applications, Wiley-Interscience, New York, NY, 1983
|
16 |
v H. Tanaka, S. Uejima and K. Asia, Linear regression analysis with Fuzzy model, IEEE Trans. Man. Cybernet. 12 (6) (1982) 903-907
DOI
ScienceOn
|
17 |
H. Tanaka, J. Watada, Possibilistic linear systems and their applications to linear regression model, Fuzzy Sets and Systems 27 (1988) 275-289
DOI
ScienceOn
|
18 |
V. Vapnik, The Nature of Statistical learning Theory, Springer, 1995
|
19 |
A. Aizerman, E. M. Braverman and L. I. Rozoner, Theoretical foundations of the potential function method in pattern recognition learning, Automation and Remote Control 25 (1964), 821-837
|
20 |
J. Watada, theory of fuzzy multivariate analysis and its applications, PH. D. Dissertation University of Osaka Prefecture, 1983
|