A data processing algorithm based on vehicle weigh-in-motion systems

Author

Nan Chen ; Quanhu Li ; Fei Li ; Zhiliang Jia

Author_Institution

Dept. of Electron. & Inf. Eng., Inner Mongolia Univ., Hohhot, China

fYear

2013

fDate

23-25 July 2013

Firstpage

227

Lastpage

231

Abstract

According to the output value of gravitational sensors and speed of vehicles, one back-propagation (BP) neural network model is established. The genetic algorithm is used to optimize the BP neural network. This method can speed up the convergence and avoid getting stuck in the local minimum. The experiment results show that the optimizing BP neural network algorithm based on genetic algorithm can reduce the average error of the calculation and prediction. And the accuracy and efficiency of the weigh-in-motion (WIM) system are improved.

Keywords

backpropagation; convergence; data handling; genetic algorithms; road vehicles; traffic engineering computing; BP neural network model; WIM system; back-propagation neural network; convergence; data processing algorithm; genetic algorithm; gravitational sensors; optimizing BP neural network algorithm; vehicle speed; vehicle weigh-in-motion systems; Accuracy; Algorithm design and analysis; Biological neural networks; Genetic algorithms; Training; Vehicles; BP neural network; genetic algorithm; optimization; the weigh-in-motion system;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation (ICNC), 2013 Ninth International Conference on

Conference_Location

Shenyang

Type

conf

DOI

10.1109/ICNC.2013.6817975

Filename

6817975