Parameters Identification on the Improved Hysteretic Preisach Model Based on the RBF Neural Network

2015 ◽  
Vol 12 (14) ◽  
pp. 5437-5450
Author(s):  
Cheng Qian
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Parameters Identification ◽  
Preisach Model ◽  
Model Based

scholarly journals Recognition Method of Drinking-driving Behaviors Based on PCA and RBF Neural Network

2018 ◽  
Vol 30 (4) ◽  
pp. 407-417
Author(s):  
Yifan Sun ◽  
Jinglei Zhang ◽  
Xiaoyuan Wang ◽  
Zhangu Wang ◽  
Jie Yu
Keyword(s):  
Neural Network ◽  
Traffic Safety ◽  
Road Traffic ◽  
Rbf Neural Network ◽  
Principal Component ◽  
Physiological Data ◽  
Recognition Model ◽  
Driving Behaviors ◽  
Model Based ◽  
Safety Research

Drinking-driving behaviors are important causes of road traffic injuries, which are serious threats to the lives and property of traffic participants. Therefore, reducing the occurrences of drinking-driving behaviors has become an important problem of traffic safety research. Forty-eight male drivers and six female drivers who could drink moderate alcohol were chosen as participants. The drivers’ physiological data, operation behavior data, car running data, and driving environment data were collected by designing various virtual traffic scenes and organizing drivers to conduct driving simulation experiments. The original variables were analyzed by the Principal Component Analysis (PCA), and seven principal components were extracted as the input vector of the Radial Basis Function (RBF) neural network. The principal component data was used to train and verify the RBF neural network. The Levenberg-Marquardt (LM) algorithm was chosen to train the parameters of the neural network and build a drinking-driving recognition model based on PCA and RBF  neural network to realize an accurate recognition of drinking-driving behaviors. The test results showed that the drinking-driving recognition model based on PCA and RBF neural network could identify drinking drivers accurately during driving process with a recognition accuracy of 92.01%, and the operation efficiency of the model was high. The research can provide useful reference for prevention and treatment of drinking and  driving and traffic safety maintenance.

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