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将过程神经网络应用于GDP预测,可以将时间序列中的时间累积效应充分考虑到预测中,能较好地解决传统GDP预测方法中的一些不足。运用黑龙江省1981年至2010年GDP与其影响因素数据,结合相关理论,建立了基于过程神经网络的GDP预测模型,对GDP进行了预测。结果表明,将过程神经网络应用于GDP预测问题中,可以得到较高的预测精度,预测效果较好。
The application of process neural network to GDP forecast can fully take time accumulation effect in time series into consideration and can well solve some shortcomings in traditional GDP forecasting methods. Based on the data of GDP and its influencing factors from 1981 to 2010 in Heilongjiang Province and the relevant theories, a GDP forecasting model based on process neural network is established and GDP is predicted. The results show that the application of process neural network to GDP forecasting problem can obtain higher forecasting accuracy and better forecasting results.