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Analysis of an impact linear relationship between input variables having on prediction of BP neural network

机译:具有BP神经网络预测的输入变量之间的影响线性关系分析

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Since the artificial neural networks were put forward, they have been used widely in predicting, and achieved good effect. But few pay attention to what an effect input variables with the linear correlation will have on the artificial neural network. Based on one example, I analyzed and studied an influence which the input variables with linear relation have on stability and prediction effect of BP neural networks predictive model. The results show that when the linear correlation between input variables is eliminated linear correlation, prediction accuracy and stability of BP neural networks can be improved.
机译:自从提出了人工神经网络以来,它们已经在预测中得到了广泛的应用,并取得了良好的效果。但是很少有人注意具有线性相关性的输入变量对人工神经网络的影响。在一个例子的基础上,我分析并研究了线性关系输入变量对BP神经网络预测模型的稳定性和预测效果的影响。结果表明,消除输入变量之间的线性相关性,可以提高BP神经网络的预测精度和稳定性。

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