吉林大学学报(理学版)

• 计算机科学 • 上一篇    下一篇

基于小波-ELM神经网络的短期停车泊位预测

陈海鹏1, 图晓航1, 王玉1,2, 郑金宇3   

  1. 1. 吉林大学 计算机科学与技术学院, 长春 130012; 2. 吉林大学 应用技术学院, 长春 130012; 3. 吉林大学 软件学院, 长春 130012
  • 收稿日期:2016-05-16 出版日期:2017-03-26 发布日期:2017-03-24
  • 通讯作者: 王玉 E-mail:wangyu001@jlu.edu.cn

ShortTerm Parking Space Prediction Based on Wavelet-ELM Neural Networks

CHEN Haipeng1, TU Xiaohang1, WANG Yu1,2, ZHENG Jinyu3   

  1. 1. College of Computer Science and Technology, Jilin University, Changchun 130012, China;2. College of Applied Technology, Jilin University, Changchun 130012, China;3. College of Software, Jilin University, Changchun 130012, China
  • Received:2016-05-16 Online:2017-03-26 Published:2017-03-24
  • Contact: WANG Yu E-mail:wangyu001@jlu.edu.cn

摘要: 采用小波变换与极限学习机(ELM)相结合的方法对短时空余停车泊位进行预测. 首先通过小波函数对有效停车泊位时间序列进行小波分解和重构; 然后用ELM对分解后所得的各时间序列进行预测; 最后对各神经网络的预测结果进行合成, 得到最终的预测结果. 预测实例结果表明, 该方法缩短了训练时间, 提高了预测结果.

关键词: 空余停车泊位, 停车泊位管理系统, 小波变换, 极限学习机

Abstract: We proposed a forecasting model of shortterm unoccupi ed parking space by using the method of combining wavelet transform with extr eme learning machine(ELM). Firstly, the time series of the effective parking spa ce were decomposed and reconstituted by wavelet function. Secondly, ELM was used to forecast the decomposed time series respectively. Final ly, the prediction results of each neural network were combined to get the final prediction results. The prediction instance results show that the method shorte ns the training time and improves the prediction results.

Key words: wavelet transform, unoccupied parking s pace, extreme learning machine (ELM), parking space management system

中图分类号: 

  • TP391