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Natural Gas Load Forecasting Based on Improved Back Propagation Neural Network

机译:基于改进的背传播神经网络的天然气负荷预测

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To suit for the condition that the relative error is more popular than the absolute error, and overcome the shortcoming of the traditional Back propagation neural network, this paper proposed an improved Back propagation algorithm with additional momentum item based on the sum of relative error square. The improved algorithm was applied to the example of the natural gas load forecasting, simulations showed that the improved algorithm has faster training speed than the traditional algorithm, and has higher accuracy as while.
机译:为了适应相对误差比绝对误差更受欢迎的条件,并克服传统背部传播神经网络的缺点,提出了一种基于相对错误方形和相对误差方的附加动量项的改进的背传播算法。改进的算法应用于天然气负荷预测的示例,模拟表明,改进的算法比传统算法更快的训练速度,并且具有更高的精度。

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