东北地区城市经济活力分析与预测

Xuan Cui, Yali Zhang, Lingyi Wei
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摘要

考虑到时间的推移,原始经济活力指数会随着社会发展的变化而变化,我们使用BP神经网络作为近十年原始GDP数据对未来30年的GDP进行预测。BP神经网络于1985年由Rumelhart提出,该算法解决了多层神经网络连接权隐层的系统学习问题[1]。它由输入层、隐藏层和输出层组成。其原理是不断调整网络权值和阈值,将误差向后传递,再对误差进行修正,以达到期望的输入输出映射。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis and Forecast of Urban Economic Vitality in Northeast China
Taking into account the passage of time, the original economic vitality index will vary with changes in social development, we use the BP neural network nearly a decade as the original GDP data for the next 30 years the GDP forecast. BP neural network in 1985, proposed by Rumelhart, the algorithm solves the system of learning problems multilayer neural network connection weights hidden layer [1].It consists of an input layer, a hidden layer, and an output layer. The principle is to continuously adjust the network weights and thresholds by transmitting errors backward and then correcting the errors to achieve the desired input-output mapping.
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