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Forecasting of Government's Financial Educational Fund by Using Neural Networks Model

机译:用神经网络模型预测政府金融教育基金

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Forecasting method using neural networks has been advocated as an alternative to traditional statistical forecasting in recent years. The paper built a feed-forward neural network model to forecast the values of government's financial educational fund (GFEF) in year 2010. On the basis of data processing, the structure of neural networks was given. The algorithm that adopted as a learning phase in the model was a fast one differing from that of the steep decent algorithm. The forecasts obtained from neural networks model were compared with the data forecasting by experts, and the error curve and the auto-adjusting curve of learning rate were also illustrated. The results show that the model was very effective.
机译:近年来,使用神经网络的预测方法作为传统统计预测的替代品。本文建立了一种前锋神经网络模型,预测2010年政府金融教育基金(GFEF)的价值。在数据处理的基础上,给出了神经网络的结构。在模型中采用的算法是与陡峭体面算法的学习阶段相差的算法。将从神经网络模型获得的预测与专家的数据预测进行了比较,并且还示出了误差曲线和学习率的自动调整曲线。结果表明,该模型非常有效。

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