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Volume 23 Issue 11
Nov.  2001
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Wei Gang, Tian Chuanjun . CONSTRUCTIONAL METHOD OF FUNCTION APPROXIMATION OF GNN[J]. Journal of Electronics & Information Technology, 2001, 23(11): 1134-1139.
Citation: Wei Gang, Tian Chuanjun . CONSTRUCTIONAL METHOD OF FUNCTION APPROXIMATION OF GNN[J]. Journal of Electronics & Information Technology, 2001, 23(11): 1134-1139.

CONSTRUCTIONAL METHOD OF FUNCTION APPROXIMATION OF GNN

  • Received Date: 2000-01-07
  • Rev Recd Date: 2000-08-24
  • Publish Date: 2001-11-19
  • In this paper, the function approximation of Gelenbe Neural Network (GNN) is discussed and it is proved that GNN can approximate any G-type polynomial by using constructional method. Number of units used by this method can be reduced to 2/(n+1) of that used by previous methods for a same G-type polynomial, where n is the degree of G-type polynomial.
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  • C. Chui, X. Li, Approximation by ridge functions and neural networks with one hidden layer,Journal of Approximation Theory, 1992, 70(2), 131-141.[2]韦岗,李华,徐秉铮,关于前馈多层神经网络多维函数逼近能力的一个定理,电子科学学刊,1997,19(4),433-438[3]韦岗,贺前华,欧阳景正,关于多层感知器的函数逼近能力,信息与控制,1996,25(6),321-324.[4]K. Hornik, Some results on neural network approximation, Neural Networks, 1993, 6(8), 1069- 1072.[5]J. Park, I. W. Sandberg, Universal approximation using radial-basis-function networks, Neural Comput., 1991, 3(2), 246-257.[6]E. Gelenbe, Y. Feng, K. R. R. Krishnan, Neural network methods for volumetric magnetic reso nance imaging of the human brain, Proc. IEEE., 1996, 84(10), 1529-1543.[7]E. Gelenbe, Random neural networks with negative and positive signals and product form solution, Neural Comput., 1989, 1(4), 502-511.[8]E. Gelenbe, Learning in the recurrent random neural network, Neural Comput., 1993, 5(1), 154- 164.[9]E. Gelenbe, A. Stafylopatis, A. Likas, Associative memory operation of the random network model, in Proc. Int. Conf. Artificial Neural Networks, Helsinki, Finland, 1991, 307-312.[10]E. Gelenbe, Z. H. Mao, Y. D. Li, Function approximation with spiked random networks, IEEE Trans. on Neural Networks, 1999, NN-10(1), 3-9.
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