基于贝叶斯与因果岭回归的物联网流量预测模型  

The flow prediction model in internet of things based on Bayesian and causal ridge regression

作　　者：陈翔[1] 唐俊勇[2] CHEN Xiang;TANG Jun-Yong(School of Civil Engineering,Xi'an Technological University,Xi'an 710021,China;School of Computer Science and Engineering,Xi'an Technological University,Xi'an 710021,China)

机构地区：[1]西安工业大学建筑工程学院,西安710021 [2]西安工业大学计算机科学与工程学院,西安710021

出　　处：《四川大学学报（自然科学版）》2018年第5期965-970,共6页Journal of Sichuan University(Natural Science Edition)

基　　金：陕西省科技厅工业科技攻关项目(2016GY-088)

摘　　要：针对物联网流量预测困难的问题,提出了一种基于贝叶斯与因果岭回归的物联网流量预测模型.该模型首先根据物联网流量传输波动影响链路变化等因果关系,深入刻画物联网流量局部特征,并利用薛定谔方程优化识别模型,同时结合贝叶斯拟合因果关系联合岭回归方法建立预测模型.最后,通过仿真实验研究了该模型与其他方法之间的性能状况,结果表明该模型在平均队列、阻塞率和延迟率等方面具有较大优势.In order to solve the flow prediction problem of Internet of Things,a flow prediction model is proposed based on Bayesian and causal ridge regression.At first,the local characteristic of flow is deeply depicted considering the causal relationship between the fluctuation of the traffic flow and the change of the link;in addition,Schrodinger equation is used to optimize the recognition model.Then,the prediction model is built with Bayesian and causal ridge regression.Finally,the performance of this model and other methods is studied by simulation experiment.The results show that this model has a great advantage in average queue,blocking rate,delay rate and so on.

关 键 词：物联网 流量 预测 贝叶斯 因果岭回归 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]