Freight Prediction Model Based on GABP Neural Network

机译：基于GABP神经网络的货运量预测模型。

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Back Propagation (BP) Neural Network has the ability of self-studying, self-adapting, fault tolerance and generalization. But there are some defaults in its basic application. Such as low convergence speed, local extremes and so on. So there are some limitations in practice. A quantitative forecast method based on the BP Neural Network improved by genetic algorithm (GA) is proposed in the paper. And the genetic algorithm is used to optimize the initial weights and threshold of BP network. The model is trained with the freight data of a city, and then it is used to forecast the freight. Form the comparison of simulated results of GABP network and these worked out by traditional BP network, it concludes that GABPNN has small error in forecasting. And it indicates that GA has the capability of fast learning the weight of network and globally search, in addition, the training speed of the improved BP network is greatly raised.

机译：反向传播（BP）神经网络具有自学习，自适应，容错和泛化的能力。但是它的基本应用程序中有一些默认设置。如收敛速度低，局部极端等。因此在实践中存在一些限制。提出了一种基于遗传算法改进的BP神经网络的定量预测方法。并利用遗传算法对BP网络的初始权重和阈值进行优化。该模型使用城市的货运数据进行训练，然后用于预测货运。通过比较GABP网络的仿真结果和传统BP网络得出的结果，可以得出GABPNN的预测误差较小的结论。这表明遗传算法具有快速学习网络权重和全局搜索的能力，并且大大提高了改进后的BP网络的训练速度。

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《Computational Intelligence and Design (ISCID), 2008 International Symposium on》||P.229-232|共4页
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Huang Yuansheng; Lin Yufang; Qiu Zilong;
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中图分类工业技术;
关键词
BP Neural Network; genetic algorithm; prediction;

机译：BP神经网络;遗传算法;预测;

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Freight Prediction Model Based on GABP Neural Network

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