-
2
-
-
55549136164
-
-
Science Press, Beijing pp. 92-93, Reprint
-
Hritonenko N., and Yatsenko Y. Mathematical Modeling in Economics, Ecology and the Environment (2006), Science Press, Beijing pp. 92-93, Reprint
-
(2006)
Mathematical Modeling in Economics, Ecology and the Environment
-
-
Hritonenko, N.1
Yatsenko, Y.2
-
3
-
-
0024861871
-
Approximation by superpositions of sigmoidal function
-
Cybenko G. Approximation by superpositions of sigmoidal function. Math. of Control Signals, and System 2 (1989) 303-314
-
(1989)
Math. of Control Signals, and System
, vol.2
, pp. 303-314
-
-
Cybenko, G.1
-
4
-
-
0024866495
-
On the approximate realization of continuous mappings by neural networks
-
Funahashi K.I. On the approximate realization of continuous mappings by neural networks. Neural Netw. 2 (1989) 183-192
-
(1989)
Neural Netw.
, vol.2
, pp. 183-192
-
-
Funahashi, K.I.1
-
5
-
-
0024880831
-
Multilayer feedforward networks are universal approximation
-
Hornik K., Stinchcombe M., and White H. Multilayer feedforward networks are universal approximation. Neural Netw. 2 (1989) 359-366
-
(1989)
Neural Netw.
, vol.2
, pp. 359-366
-
-
Hornik, K.1
Stinchcombe, M.2
White, H.3
-
6
-
-
0025627940
-
Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks
-
Hornik K., Stinchcombe M., and White H. Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks. Neural Netw. 3 (1990) 551-560
-
(1990)
Neural Netw.
, vol.3
, pp. 551-560
-
-
Hornik, K.1
Stinchcombe, M.2
White, H.3
-
7
-
-
0027262895
-
Multilayer feedforward networks with a nonpolynomial activation function can approximate any function
-
Leshno M., Lin V.Y., Pinks A., and Schocken S. Multilayer feedforward networks with a nonpolynomial activation function can approximate any function. Neural Netw. 6 (1993) 861-867
-
(1993)
Neural Netw.
, vol.6
, pp. 861-867
-
-
Leshno, M.1
Lin, V.Y.2
Pinks, A.3
Schocken, S.4
-
8
-
-
0000358945
-
Approximation by superposition of a sigmoidal function
-
Mhaskar H.N., and Micchelli C.A. Approximation by superposition of a sigmoidal function. Adv. Appl. Math. 13 (1992) 350-373
-
(1992)
Adv. Appl. Math.
, vol.13
, pp. 350-373
-
-
Mhaskar, H.N.1
Micchelli, C.A.2
-
9
-
-
0000378922
-
Approximation by ridge functions and neural networks with one hidden layer
-
Chui C.K., and Li X. Approximation by ridge functions and neural networks with one hidden layer. J. Approx. Theory 70 (1992) 131-141
-
(1992)
J. Approx. Theory
, vol.70
, pp. 131-141
-
-
Chui, C.K.1
Li, X.2
-
10
-
-
0029343809
-
Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to a dynamic system
-
Chen T.P., and Chen H. Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to a dynamic system. IEEE Trans. Neural Netw. 6 (1995) 911-917
-
(1995)
IEEE Trans. Neural Netw.
, vol.6
, pp. 911-917
-
-
Chen, T.P.1
Chen, H.2
-
11
-
-
9644287964
-
An approximation by neural networks with a fixed weight
-
Hahm N., and Hong B.I. An approximation by neural networks with a fixed weight. Comput. & Math. Appl. 47 (2004) 1897-1903
-
(2004)
Comput. & Math. Appl.
, vol.47
, pp. 1897-1903
-
-
Hahm, N.1
Hong, B.I.2
-
12
-
-
0027599793
-
Universal approximation bounds for superpositions of a sigmoidal function
-
Barron A.R. Universal approximation bounds for superpositions of a sigmoidal function. IEEE Trans. Inform. Theory 39 (1993) 930-945
-
(1993)
IEEE Trans. Inform. Theory
, vol.39
, pp. 930-945
-
-
Barron, A.R.1
-
13
-
-
0000194429
-
Degree of approximation by neural networks with a single hidden layer
-
Mhaskar H.N., and Miccheli C.A. Degree of approximation by neural networks with a single hidden layer. Adv. Appl. Math. 16 (1995) 151-183
-
(1995)
Adv. Appl. Math.
, vol.16
, pp. 151-183
-
-
Mhaskar, H.N.1
Miccheli, C.A.2
-
14
-
-
0032144406
-
Constructive function approximation by three-layer artificial neural networks
-
Suzuki S. Constructive function approximation by three-layer artificial neural networks. Neural Netw. 11 (1998) 1049-1058
-
(1998)
Neural Netw.
, vol.11
, pp. 1049-1058
-
-
Suzuki, S.1
-
16
-
-
0001574595
-
Uniform approximation by neural networks
-
Makovoz Y. Uniform approximation by neural networks. J. Approx. Theory 95 (1998) 215-228
-
(1998)
J. Approx. Theory
, vol.95
, pp. 215-228
-
-
Makovoz, Y.1
-
17
-
-
13844255524
-
Smooth function approximation using neural networks
-
Ferrari S., and Stengel R.F. Smooth function approximation using neural networks. IEEE Trans. Neural Networks 16 (2005) 24-38
-
(2005)
IEEE Trans. Neural Networks
, vol.16
, pp. 24-38
-
-
Ferrari, S.1
Stengel, R.F.2
-
18
-
-
24344496437
-
The essential order of approximation for neural networks
-
Xu Z.B., and Cao F.L. The essential order of approximation for neural networks. Sci. China Ser. F 47 (2004) 97-112
-
(2004)
Sci. China Ser. F
, vol.47
, pp. 97-112
-
-
Xu, Z.B.1
Cao, F.L.2
-
19
-
-
0031195377
-
Rate of convergence of some neural network operators to the unit-univariate case
-
Anastassiou G.A. Rate of convergence of some neural network operators to the unit-univariate case. J. Math. Anal. Appl. 212 (1997) 237-262
-
(1997)
J. Math. Anal. Appl.
, vol.212
, pp. 237-262
-
-
Anastassiou, G.A.1
-
20
-
-
38649094938
-
The estimate for approximation error of neural networks: A constructive approach
-
Cao F.L., Xie T.F., and Xu Z.B. The estimate for approximation error of neural networks: A constructive approach. Neurocomputing 71 (2008) 626-630
-
(2008)
Neurocomputing
, vol.71
, pp. 626-630
-
-
Cao, F.L.1
Xie, T.F.2
Xu, Z.B.3
-
23
-
-
84953751498
-
-
Cambridge University Press, Cambridge
-
Zygmund A. Trigonometric Series (1959), Cambridge University Press, Cambridge
-
(1959)
Trigonometric Series
-
-
Zygmund, A.1
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