CONSTRUCTIONAL METHOD OF FUNCTION APPROXIMATION OF GNN
G神经网络函数映射能力的构造性证明

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.