本文采用了反向传播网络进行系统辨识。
As far as system identification is concerned, the back-propagation network is used in the paper.
结论反向传播网络在函数逼近方面差的原因是激励函数的全局性、隐层结点数目的不确定性。
Conclusion Because of the inspirit function's globaling and the number of the Hidden Layer'node uncertainty the BPNN was not done well.
常规的反向传播网络（BP）是一种内部呈完全联结的全局性网络，它对非平滑系统的学习能力较弱。
Regular back-propagation networks (BP) are fully connected globalized neural networks, it is usually difficult for them to approximate illbehaved systems, which exist in any application field.
本文将神经网络引入到航材需求分析领域中，应用误差反向传播网络建立模型进行预测，并对模型结果进行了分析。
This paper introduces Neural net to the fields of air-materials demands analysis, and applies Back Propagation network to forecast.
比较而言，学习矢量量化网络和概率神经网络在分类能力方面要比反向传播网络好一些，概率神经网络在计算负载方面比学习矢量量化网络要更胜一筹。
By comparison, LVQ network and PNN network are better than BPN network in classification ability, and PNN network is better than the others in computation load.
比较而言，学习矢量量化网络和概率神经网络在分类能力方面要比反向传播网络好一些，概率神经网络在计算负载方面比学习矢量量化网络要更胜一筹。
By comparison, LVQ network and PNN network are better than BPN network in classification ability, and PNN network is better than the others in computation load.
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