1 |
C. K. Chui, X. Li and H. N. Mhaskar, Limitations of the approximation capabilities of neural networks with one hidden layer, Adv. Comput. Math., 5 (1996), 233-243.
DOI
ScienceOn
|
2 |
B. Gao and Y. Xu, Univariant approximation by superpositions of a sigmoidal function, J. Math. Anal. Appl., 178 (1993), 221-226.
DOI
ScienceOn
|
3 |
N. Hahm and B. I. Hong, Extension of localised approximation by neural networks, Bull. Austral. Math. Soc., 59 (1999), 121-131.
DOI
|
4 |
N. Hahm and B. I. Hong, An approximation by neural networks with a fixed weight, Comput. Math. Appl., 47 (2004), 1897-1903.
DOI
ScienceOn
|
5 |
N. Hahm and B. I. Hong, Approximation order to a function in space by generalized translation networks, Honam Math. J. 28(1) (2006), 125-133.
|
6 |
N. Hahm and B. I. Hong, A simultaneous neural network approximation with the squashing function, Honam Math. J. 31(2) (2009), 147-156.
과학기술학회마을
DOI
ScienceOn
|
7 |
B. I. Hong and N. Hahm, Approximation order to a function in C(R) by superposition of a sigmoidal function, Appl. Math. Lett., 15 (2002), 591-597.
DOI
ScienceOn
|
8 |
B. L. Kalman and S. C. Kwasny, Why Tanh : Choosing a sigmoidal function, Int. Joint Conf. on Neural Networks 4 (1992), 578-581.
|
9 |
M. Leshno, V. Lin, A. Pinkus and S. Schocken, Multilayered feedforward networks with a nonpolynomial activation function can approximate any function, Neural Networks, 6 (1993), 61-80.
|
10 |
G. Lewicki and G. Marino, Approximation of functions of finite variation by superpositions of a sigmoidal function, Appl. Math. Lett. 17 (2004), 1147-1152.
DOI
ScienceOn
|
11 |
H. N. Mhaskar and N. Hahm, Neural networks for functional approximation and system identification, Neural Comput., 9 (1997), 143-159.
DOI
ScienceOn
|
12 |
L. L. Schumaker, Spline Functions : Basic Theory, Cambridge University Press, Cambridge, 2007.
|