• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Chinese Automation Congress >Research on Trajectory Prediction Method of Mobile Pollution Source Based on Hybrid Genetic Particle Swarm and optimized Extreme Learning Machine
【24h】

Research on Trajectory Prediction Method of Mobile Pollution Source Based on Hybrid Genetic Particle Swarm and optimized Extreme Learning Machine

机译：基于混合遗传粒子群和优化极限学习机的移动污染源轨迹预测方法研究

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In order to accurately predict the trajectory of mobile pollution sources such as motor vehicles in real time, a trajectory prediction method based on hybrid genetic particle swarm optimization and optimized extreme learning machine (HGPSO-OELM) is proposed in this paper. optimized Extreme Learning Machine (OELM) avoids the disadvantage of traditional Extreme Learning Machine (ELM) which has poor generalization performance for small data sets. However, due to the random assignment of input weights and hidden layer node biases parameter groups, the prediction accuracy is affected. Therefore, finding the optimal parameter group can improve the prediction accuracy of vehicle trajectory. By introducing the hybrid genetic particle swarm optimization (HGPSO) algorithm, the optimal parameters of OELM model are dynamically optimized, which overcomes the randomness of the model establishment. Only a small number of hidden layer neurons are needed to achieve better prediction performance and improve the generalization of the network. Using the vehicle GPS trajectory data provided by ACM SIGS PATIAL GIS 2012, this paper chooses different historical data lengths based on ELM, OELM, HMM, GA-BPNN, LSTM, HGPSO-OELM and other methods to compare the one-step prediction performance under different historical data lengths. The experimental results show that the proposed HGPSO-OELM algorithm has higher prediction accuracy and real-time performance. The single-step prediction accuracy is the best when the length of historical data sequence is 20, and the multi-step time series prediction is realized under this length.
机译：为了实时准确地预测机动车辆等污染源的运动轨迹，提出了一种基于混合遗传粒子群优化算法和优化极限学习机（HGPSO-OELM）的运动轨迹预测方法。优化的极限学习机（OELM）避免了传统极限学习机（ELM）的缺点，后者对于小数据集的泛化性能很差。但是，由于输入权重的随机分配和隐藏层节点对参数组的偏见，影响了预测精度。因此，找到最优参数组可以提高车辆轨迹的预测精度。通过引入混合遗传粒子群算法（HGPSO），动态优化了OELM模型的最优参数，克服了模型建立的随机性。仅需要少量的隐藏层神经元即可实现更好的预测性能并改善网络的泛化。本文利用ACM SIGS PATIAL GIS 2012提供的车辆GPS轨迹数据，根据ELM，OELM，HMM，GA-BPNN，LSTM，HGPSO-OELM等方法选择不同的历史数据长度，以比较在以下情况下的单步预测性能不同的历史数据长度。实验结果表明，所提出的HGPSO-OELM算法具有较高的预测精度和实时性。当历史数据序列的长度为20时，单步预测精度最高，在此长度下可以实现多步时间序列预测。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号