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Research on Railway Passenger Flow Prediction Method Based on GA Improved BP Neural Network

机译:基于GA改进BP神经网络的铁路客流预测方法研究。

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This paper chooses passenger flow data of some stations in China from January 2015 to March 2016, and the time series prediction model of BP neural network for railway passenger flow is established. But because of its slow convergence speed and easily falling into local optimal solution of the problem, we propose to improve the time series model of BP neural network by genetic algorithm to predict railway passenger flow. Experimental results show that the improved method has higher prediction accuracy and better nonlinear fitting ability.
机译:本文选取2015年1月至2016年3月中国部分车站的客流数据,建立了BP神经网络的铁路客流时间序列预测模型。但由于收敛速度慢,容易陷入问题的局部最优解,我们提出用遗传算法改进BP神经网络的时间序列模型,以预测铁路客流。实验结果表明,改进的方法具有较高的预测精度和较好的非线性拟合能力。

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