SVM与神经网络组合模型中的短时交通流预测分析  

Analysis of Short term Traffic Flow Prediction in SVM and Neural Network Combination Models

作　　者：邢盼盼 李春梅 XING Panpan;LI Chunmei(Xuchang Electrical Vocational College,Henan 461000,China)

机构地区：[1]许昌电气职业学院,河南461000

出　　处：《电子技术（上海）》2023年第12期387-389,共3页Electronic Technology

摘　　要：阐述短时交通流预测方法的特点,将支持向量机(SVM)与神经网络相结合,综合运用两者对模型解析的优势,构建短时交通流分析的组合模型,结合交通流数据的训练情况,对预测模型的精度进行控制,使交通流分析过程能够顺利进行。This paper expounds the characteristics of short-term traffic flow prediction methods,combining support vector machines(SVM)with neural networks,and comprehensively utilizing the advantages of both in model analysis to construct a combination model for short-term traffic flow analysis.Combined with the training situation of traffic flow data,the accuracy of the prediction model is controlled to ensure the smooth progress of the traffic flow analysis process.

关 键 词：智能算法 SVM 神经网络 组合模型 短时交通 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] U491.14[自动化与计算机技术—控制科学与工程]