以人工神经网络的前馈型网络为基础结构，基于反向传播算法进行学习和训练来拟和证券价格指数的运动趋势。

In this model, back propagation algorithm based on forward networks was conducted to learn information of historical data and to train the network weights.

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实验结果表明前馈后向传播网络的性能最好，与基准模型比较平均错误率下降54.4%。

Experiment results show that feed-forward backpropagation network achieves the best performance, which reduces average error rate by54.4%.

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将以误差反向传播为训练算法的前馈式人工神经网络（ BP- ANN）首次用于中草药的裂解气相色谱谱图解析。

The potential utility of feed forward artificial neural network using the back propagation algorithm (BP-ANN), in interpreting pyrogram data from traditional Chinese medicine was discussed.

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