计算机与数字工程2024，Vol.52Issue(4)：1257-1263,7.DOI:10.3969/j.issn.1672-9722.2024.04.052
门控循环单元网络下的空气污染物预测模型
Air Pollutants Prediction Model Based on Gated Recurrent Unit Neural Network
摘要
Abstract
In this paper,aiming at the exitsing methods of ambient air pollutants prediction are based on a single data set and a shallow neural network,and the data information hidden in the time series can't be fully exploited.A time series prediction based on gated recurrent unit network is proposed.Firstly,a filling algorithm is designed for the missing values of time series.Then the su-pervised experiment is set up to adjust the parameters of batch size and training step,training optimization algorithm,network weight initialization and Dropout regularization,and the length and time are comparison of parameters of long short-term memory network.Finally,the verificaton and analysis are carried out,and the parameters are compared with the long-term memory time re-current neural network.The research results show that compared with long-term memory time recurrent neural network,threshold loop unit network not only has a faster training time,but also has a more significant in air pollutant prediction performance,which is a feasible and effective prediction method.关键词
门控循环单元网络/时间递归神经网络/时间序列/深度学习/缺失值算法Key words
gated recurrent unit network/time recurrent neural network/time series/deep learning/missing value algo-rithm分类
信息技术与安全科学引用本文复制引用
刘栩粼,谢崇波..门控循环单元网络下的空气污染物预测模型[J].计算机与数字工程,2024,52(4):1257-1263,7.基金项目
四川信息职业技术学院青年科研基金项目(编号:2020C24)资助. （编号:2020C24）
