Modeling with Neural Networks: Principles and Model Design Methodology - Neural Networks: Methodology and Applications - page 199

References
1. Akaike H. [1973], Information theory and an extension of the maximum likeli-
hood princiaple, 2 nd International Symposium on Information Theory , pp 267-
281, Akademia Kiado
2. Akaike H. [1974], A new look at the statistical model identification, IEEE Trans-
actions on Automatic Control , 19, pp 716-723
3. Antoniadis A., Berruyer J., Carmona R. [1992], Regression non lineaire et ap-
plications , Economica
4. Bartlett P.L. [1997], For valid generalization, the size of the weights is more
important than the size of the network, Neural Information Processing Systems ,
9, Morgan Kaufmann
5. Bishop C. [1993], Curvature-driven smoothing: a learning algorithm for feedfor-
ward networks, IEEE Transactions on Neural Networks , 4, pp 882-884
6. Bishop C. [1995], Neural Networks for Pattern Recognition , Oxford University
Press.
7. Bjorck A. [1967], Solving linear least squares problems by Gram-Schmidt or-
thogonalization. BIT , 7, pp 1-27
8. Broyden C.G. [1970], The convergence of a class of double-rank minimization
algorithms 2: the new algorithm, Journal of the Institute of Mathematics and
its Applications, 6, pp 222-231
9. Chen S., Billings S.A., Luo W., Orthogonal least squares methods and their
application to nonlinear system identification, International Journal of Control ,
50, pp 1873-1896
10. Draper N.R., Smith H. [1998], Applied Regression Analysis , Wiley
11. Dreyfus G., Idan Y. [1998], The canonical form of discrete-time nonlinear mod-
els, Neural Computation , 10, pp 133-164
12. Gallinari P., Cibas T. [1999], Practical complexity control in multilayer percep-
trons, Signal Processing , 74, pp 29-46
13. Geman S., Benenstock E., Doursat R. [1992], Neural networks and the
bias/variance dilemma, Neural Computation 4, pp 1-58
14. Goodwin G.C., Payne R.L. [1977], Dynamic System Identification: Experiment
Design and Data Analysis , Mathematics in Science and Engineering, Academic
Press
15. Goodwin G.C., Sin K.S. [1984], Adaptive Filtering Prediction and Control ,
Prentice-Hall, New Jersey
16. Guyon I., Gunn S., Nikravesh M., Zadeh L., eds. [2005], Feature extraction,
foundations and applications, Springer
17. Hansen L.K., Larsen J. [1996], Linear unlearning for cross-validation, Advances
in Computational Mathematics , 5, pp 269-280
18. Haykin S. [1994], Neural Networks: a comprehensive approach , MacMillan
19. Jollife I.T. [1986], Principal Component Analysis , Springer
20. Kohonen T. [2001] Self-Organizing Maps , Springer
21. Kullback S., Leibler R. A. [1951], On information and su ciency, Annals of
mathematical Statistics , 22, pp 79-86
22. Kullback S. [1959], Information Theory and Statistics , Dover Publications
23. Kuo B. C. [1992], Digital Control Systems , Saunders College Publishing
24. Kuo B. C. [1995], Automatic Control Systems , Prentice Hall
25. Lagarde de J. [1983], Initiation a l'analyse des donnees , Dunod, Paris