On the approximation by neural networks with bounded number of neurons in hidden layers

被引:75
作者
Ismailov, Vugar E. [1 ]
机构
[1] Azerbaijan Natl Acad Sci, Inst Math & Mech, AZ-1141 Baku, Azerbaijan
关键词
Neural network; MLP model; Sigmoidal function; Approximation; Superposition; MULTILAYER FEEDFORWARD NETWORKS; SUPERPOSITION THEOREM; RIDGE FUNCTIONS; METRIC-SPACES; KOLMOGOROV; REPRESENTATION; VARIABLES; DIMENSION;
D O I
10.1016/j.jmaa.2014.03.092
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
In the current note, we show that a two hidden layer neural network with d inputs, d neurons in the first hidden layer, 2d+2 neurons in the second hidden layer and with a specifically constructed sigmoidal and infinitely differentiable activation function can approximate any continuous multivariate function with arbitrary accuracy. (C) 2014 Elsevier Inc. All rights reserved.
引用
收藏
页码:963 / 969
页数:7
相关论文
共 28 条
[1]  
[Anonymous], P IEEE 1 ANN INT C N
[2]  
[Anonymous], TRANSL MATH MONOGR
[3]  
Arnold V.I., 1963, Amer. Math. Soc. Transl., V28, P51
[4]   APPROXIMATION AND ESTIMATION BOUNDS FOR ARTIFICIAL NEURAL NETWORKS [J].
BARRON, AR .
MACHINE LEARNING, 1994, 14 (01) :115-133
[5]   The estimate for approximation error of neural networks: A constructive approach [J].
Cao, Feilong ;
Xie, Tingfan ;
Xu, Zongben .
NEUROCOMPUTING, 2008, 71 (4-6) :626-630
[6]   APPROXIMATIONS OF CONTINUOUS FUNCTIONALS BY NEURAL NETWORKS WITH APPLICATION TO DYNAMIC-SYSTEMS [J].
CHEN, TP ;
CHEN, H .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 1993, 4 (06) :910-918
[7]   APPROXIMATION BY RIDGE FUNCTIONS AND NEURAL NETWORKS WITH ONE HIDDEN LAYER [J].
CHUI, CK ;
LI, X .
JOURNAL OF APPROXIMATION THEORY, 1992, 70 (02) :131-141
[8]  
Cybenko G., 1989, Mathematics of Control, Signals, and Systems, V2, P303, DOI 10.1007/BF02551274
[9]  
FRIDMAN BL, 1967, DOKL AKAD NAUK SSSR+, V177, P1019
[10]   Representation Properties of Networks: Kolmogorov's Theorem Is Irrelevant [J].
Girosi, Federico ;
Poggio, Tomaso .
NEURAL COMPUTATION, 1989, 1 (04) :465-469