In this paper, by using the gray system theory and the dynamic BP neural network, the combination forecasting model are discussed.

对时间序列的一类预测模型进行了研究，把灰色模型与BP神经网络模型组合建模，通过实例分析取得好的效果。

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In order to reduce the operation cost and optimize the unit commitment, the fast algorithm about unit commitment based on revised BP ANN (Artificial Neural Network) and dynamic search is discussed.

为了使机组达到最优组合，减少运行成本，研究了基于修正BP人工神经网络与动态搜索的快速算法在机组组合中的运用。

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To test the dynamic property, this pneumatic fatigue test system was identified by a recursive BP neural network.

为了考察该系统的动态性能，采用递归BP神经网络对该系统进行辨识。

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