群智感知环境中基于GRU网络的用户位置预测模型  

A user location prediction model based onGRU network in crowd sensing environment

作　　者：张安冉 廖祎玮[1] 赵国生[1] 王健[2] ZHANG An-ran;LIAO Yi-wei;ZHAO Guo-sheng;WANG Jian(College of Computer Science and Information Engineering,Harbin Normal University,Harbin 150025;School of Computer Science and Technology,Harbin University of Science and Technology,Harbin 150080,China)

机构地区：[1]哈尔滨师范大学计算机科学与信息工程学院,黑龙江哈尔滨150025 [2]哈尔滨理工大学计算机科学与技术学院,黑龙江哈尔滨150080

出　　处：《计算机工程与科学》2021年第10期1750-1757,共8页Computer Engineering & Science

基　　金：国家自然科学基金(61202458,61403109);黑龙江省自然科学基金(F2017021);哈尔滨市科技创新人才研究专项资金(2016RAQXJ036)。

摘　　要：在感知区域内用户分布稀疏的情况下,提前预测用户的位置是群智感知系统提高任务完成率的关键。提出了一种基于门控循环单元的用户位置预测模型。首先,构建了群智感知系统模型,实现了基于位置的参与式感知应用。然后,将用户位置的数据集做归一化处理,并结合用户历史位置数据的多维度特征构建了门控循环单元结构。最后,利用车联网中实际轨迹数据集对模型进行训练,并采用Adam算法对基于门控循环单元的用户位置预测模型的性能参数进行了优化。仿真结果表明,相比于RNN模型和LSTM模型,所提模型的预测均方误差分别降低了22%和18%,且在处理序列数据方面具有可实施性强的优势。In the case of the sparse distribution of users in the perception area,predicting the user's location in advance is the key to improve the task completion rate of the crowd sensing system.This paper presents a user location prediction model based on the gated recurrent unit.Firstly,a model of the crowd sensing system is constructed,and the application of the participatory sensing based on location is realized.Secondly,the data set of the user's location is normalized,and by combining the multidimensional characteristics of the user's historical location data,a gated recurrent unit structure is constructed.Finally,the actual trajectory data set in the vehicles networks is used to train the model,and the Adam algorithm is used to optimize the performance parameters of the user position prediction model based on the gated recurrent unit.The simulation results show that,compared with the RNN model and the LSTM model,the prediction mean square error of the proposed model is reduced by 22%and 18%respectively,and has the advantage of strong implementability in processing sequence data.

关 键 词：群智感知 门控循环单元 位置预测 Adam算法 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]