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

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 exp...

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Bibliographic Details
Published inProceedings (International Conference on Natural Computation. Print) pp. 227 - 231
Main Authors Chen, Nan, Li, Quanhu, Li, Fei, Jia, Zhiliang
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 01.07.2013
Subjects
Accuracy
Algorithm design and analysis
Algorithms
Biological neural networks
BP neural network
Computational efficiency
Conferences
Convergence
genetic algorithm
Genetic algorithms
Neural networks
Optimization
the weigh-in-motion system
Training
Vehicles
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ISSN2157-9555
2157-9563
DOI10.1109/ICNC.2013.6817975

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Summary: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.
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ISSN:2157-9555
2157-9563
DOI:10.1109/ICNC.2013.6817975