结果表明：基于神经网络组合预测模型不仅比单一预测方法能够从整体上提高预测精度，而且能避免最优组合预测模型有时出现负权重的不足。

The result shows that this combined model can improve the precision of forecasting and avoid the shortage of negative weight in the process of optimal mix forecasting model.

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并对外汇汇率数据进行了模型构造和预测。 结果表明，组合神经网络在模型的拟合精度和预测准确性方面都有提高。

Through constructing models and making predictions for the currency exchange rate data, we can see that the predictions of combined neural networks are improved.

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对比发现，利用组合灰色神经网络模型预测的位移值较单独的灰色模型预测的位移值具有更高的精度。

It is more accurate of the forecasting results by the composite gray neural network model than that by the only gray models by comparison.

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