基于PSO-BP神经网络的新能源汽车销量预测模型
DOI:
作者:
作者单位:

1.广西科技大学;2.广西科技大学经济与管理学院

作者简介:

通讯作者:

中图分类号:

TP391

基金项目:

国家自然科学(52074061);“广西科技基地与人才专项”(桂科2022AC21084);广西教育厅中青年教师科研基础能力提升项目(2024KY0347);广西科技大学博士(校科博21S07,校科博23S04)


PSO-BP Neural Network based New energy Vehicle Sales Prediction Model
Author:
Affiliation:

Guangxi University of Science and Technology, School of Economics and Management

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    为有效避免新能源汽车销量产销不平衡问题,通过PSO算法优化BP神经网络的参数迭代过程,弥补优化原本BP神经网络易陷入局部最优和收敛速度较慢的缺陷,构建了基于PSO-BP神经网络的新能源汽车销量预测模型,以比亚迪为例进行指数平滑法预测、BP和PSO-BP神经网络预测。结果表明BP神经网络模型相比于指数平滑模型在MSE,MAE和MAPE指标上预测性能优势显著,经过粒子群算法优化后的BP神经网络模型的MSE下降近7×10^7,MAE下降3 346,MAPE下降1.71%。可见基于PSO-BP神经网络的新能源汽车销量预测模型优于指数平滑模型和BP神经网络模型,粒子群优化的BP神经网络能够使模型跳出局部最优,加快收敛速度,预测结果的误差率更低,精度更高,且对企业的计划和生产具有指导作用。

    Abstract:

    In order to effectively avoid the problem of imbalance between production and sales of new energy vehicles, the PSO algorithm is used to optimize the parameter iteration process of the BP neural network, to make up for the shortcomings of optimizing the original BP neural network which is easy to fall into the local optimum and slow convergence, and to construct a new energy vehicle sales prediction model based on the PSO-BP neural network, and to take BYD as an example to carry out the prediction of the exponential smoothing method, the BP and PSO-BP neural network. The prediction of BYD, BP and PSO-BP neural network are carried out. The results show that the BP neural network model has a significant advantage over the exponential smoothing model in terms of MSE, MAE and MAPE indicators, and the BP neural network model optimized by the particle swarm algorithm shows a decrease of nearly 7×10^7 in MSE, 3 346 in MAE and 1.71% in MAPE. It can be seen that the new energy vehicle sales prediction model based on PSO-BP neural network is better than the exponential smoothing model and BP neural network model, particle swarm optimized BP neural network can make the model jump out of the local optimum, accelerate the convergence speed, the prediction results of the error rate is lower, the accuracy is higher, and it has a guiding role in the planning and production of enterprises.

    参考文献
    相似文献
    引证文献
引用本文

郝同铮,王训洪,马聪. 基于PSO-BP神经网络的新能源汽车销量预测模型[J]. 科学技术与工程, , ():

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2023-10-28
  • 最后修改日期:2024-05-22
  • 录用日期:2024-05-29
  • 在线发布日期:
  • 出版日期:
×
亟待确认版面费归属稿件,敬请作者关注