摘要
针对我国经济领域中对国外大宗商品的依赖程度高,给经济的波动带来极大影响的问题,提出一种改进神经网络的大宗商品价格预测模型。在该模型中,首先对BP神经网络的结果和流程进行概述,然后提出GA算法优化的思路,并给出了优化的具体步骤;最后采用仿真的方式,结果表明本文构建的算法与实际值之间的差距较小,具有一定的借鉴价值。
In view of the high dependence on foreign commodities in China's economic field,which has a great impact on economic fluctuation,an improved neural network model for commodity price forecasting is proposed.In this model,firstly,the results and flow of BP neural network are summarized,then the idea of GA algorithm optimization is put forward,and the specific steps of optimization are given.Finally,using simulation method,the results show that the difference between the algorithm and the actual value is small,which has a certain reference value.
作者
计大威
JI Da-wei(Shanghai Technical Institute of Electronics&Information,Shanghai 201411 China)
出处
《自动化技术与应用》
2020年第6期58-61,共4页
Techniques of Automation and Applications
