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2015 | OriginalPaper | Chapter

64. Vehicle Weigh-in-motion Systems Based on Particle Swarm Optimization

Authors : Huijuan Ding, Quanhu Li, Ting Xu, Nengshao Li

Published in: Proceedings of the Second International Conference on Mechatronics and Automatic Control

Publisher: Springer International Publishing

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Abstract

In this chapter, the particle swarm optimization (PSO) algorithm based on the random global optimization is introduced into the network training in order to eliminate the problem in back-propagation (BP) neural networks of vehicle weigh-in-motion (WIM) systems which are sensitive to the initial weights, easy to fall into the local minimum, and have a slow convergence rate. We have established a PSO-BP neural network model to optimize the initial weighted threshold and structure of the neural network. Simulation results show that the PSO-BP neural network model has faster convergence rate and a higher precision.

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Li Li Niu B. Particle swarm optimization algorithm. Beijing: Metallurgical industry press; 2009. p. 108–11. (In Chinese)

Chang Q, Zhang Y, Yu Z. Application of BP neural network to dynamic weighing with cars. J Electron Qual. 2011;4(5):37–8. (In Chinese)MATH

Ling J. Study on the design theory of highway weigh-in-motion system. Chang ’an: Chang ’an University; 2001. (In Chinese)

Liang Y, Wu C, Shi X, Ge H. Theory and application of swarm intelligence optimization algorithm. Beijing: Science Press; 2009. p. 76–8. (In Chinese)

Liu Y, Niu B. Theory and application of a new particle swarm optimization. Beijing: Science press; 2013. p. 15–8. (In Chinese)

Ai Y. The modeling and application in stock market forecast based on neural network with particle swarm optimization. Hefei: Hefei University of Technology; 2009. (In Chinese)

Yang M. Research on the algorithm of vehicle weigh-in-motion system. Chang ’an: Chang ’an University; 2010. (In Chinese)

Title: Vehicle Weigh-in-motion Systems Based on Particle Swarm Optimization
Authors: Huijuan Ding
Quanhu Li
Ting Xu
Nengshao Li
Publisher: Springer International Publishing
Book: Proceedings of the Second International Conference on Mechatronics and Automatic Control
Print ISBN: 978-3-319-13706-3

Electronic ISBN: 978-3-319-13707-0

Copyright Year: 2015
DOI: https://doi.org/10.1007/978-3-319-13707-0_64