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.

该文研究了G神经网络的函数映射能力，给出了前馈g神经网络映射任意G型多项式的构造性证明。

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In this paper, a method for converting GPS height to normal one by means of support vector machine is proposed, and compared with the methods of neural network, polynomial fitting etc.

结合GPS测量和水准测量资料，利用支持向量机方法对GPS高程进行了转换，并与神经网络和多项式拟合等拟合的结果进行了比较，得出了一些有益的结论。

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Finally, the experiment results compared with the BP neural network algorithm and polynomial matching algorithm show that the new model improves color conversion accuracy effectively.

最后，本算法与BP神经网络和多项式拟合算法比较，色彩转换精度有明显提高。

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