A SELF-LEARNING ALGORITHM FOR PREDICTING THE RUNNING VEHICLE ATTITUDE

被引：0
|
作者：
Wang, Li [2 ]
Liang, Mingzhi [2 ]
Xiang, Huaikun [1 ,3 ]
机构：
[1] Shenzhen Polytech, Shenzhen, Peoples R China
[2] Civil Aviat Univ China, Tianjin, Peoples R China
[3] Harbin Inst Technol, Shenzhen Grad Sch, Shenzhen Key Lab Urban Planning & Decis Making, Shenzhen, Peoples R China
来源：
2014 IEEE 3rd International Conference on Cloud Computing and Intelligence Systems (CCIS) | 2014年
关键词：
Vehicle attitude; Elman NN; Vehicle active safety; NEURAL-NETWORK;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Modeling and anomalous early-warning of vehicle attitude is an important element of proactive safety management of transport vehicle. However, there are great many uncertain factors for a running vehicle, which causes the anomalous early-warning unable to be realized efficiently. In view of this problem, a driving cycle self-learning system is set up upon the analysis of the vehicle running traits. And a methodology to collect the predicting the running vehicle attitude based on Elman neural network was presented. Experimental results show that the future driving cycle can be adequately represented and compared with traditional linear model and BP neural network model, this model has higher precision and better adaptability.
引用
收藏
页码：41 / 45
页数：5
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