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2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application
Study on Weigh-in-Motion System Based on Chaos Immune Algorithm and RBF Network
December 19-December 20
ISBN: 978-0-7695-3490-9
| ASCII Text | x | ||
| Yi Shen, Yunfeng Bu, Mingxin Yuan, "Study on Weigh-in-Motion System Based on Chaos Immune Algorithm and RBF Network," Pacific-Asia Workshop on Computational Intelligence and Industrial Application, IEEE, vol. 2, pp. 502-506, 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, 2008. | |||
| BibTex | x | ||
| @article{ 10.1109/PACIIA.2008.233, author = {Yi Shen and Yunfeng Bu and Mingxin Yuan}, title = {Study on Weigh-in-Motion System Based on Chaos Immune Algorithm and RBF Network}, journal ={Pacific-Asia Workshop on Computational Intelligence and Industrial Application, IEEE}, volume = {2}, year = {2008}, isbn = {978-0-7695-3490-9}, pages = {502-506}, doi = {http://doi.ieeecomputersociety.org/10.1109/PACIIA.2008.233}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Pacific-Asia Workshop on Computational Intelligence and Industrial Application, IEEE TI - Study on Weigh-in-Motion System Based on Chaos Immune Algorithm and RBF Network SN - 978-0-7695-3490-9 SP502 EP506 A1 - Yi Shen, A1 - Yunfeng Bu, A1 - Mingxin Yuan, PY - 2008 KW - weigh-in-motion KW - chaos immune algorithm KW - RBF network VL - 2 JA - Pacific-Asia Workshop on Computational Intelligence and Industrial Application, IEEE ER - | |||
Aiming at the complexity of data processing in Weigh-In-Motion (WIM) system, a nonlinear system model is built for the WIM system with radical basic function (RBF) neural network. To achieve more accurate network weights of RBF and improve the model detection precision, a novel chaos immune algorithm is presented to optimize the RBF network weights. In this paper, the logistic equation is used to generate the initial population and the chaos disturbance is used to improve the searching efficiency of immune algorithm. Experiment results show that this nonlinear model is effective, and it can reduce the detection error for nonlinearity and time-varying of WIM system and improve the detection precision. Compared to the simple RBF network model, the proposed RBF network optimized by chaos immune algorithm owns high measuring precision.
Index Terms:
weigh-in-motion, chaos immune algorithm, RBF network
Citation:
Yi Shen, Yunfeng Bu, Mingxin Yuan, "Study on Weigh-in-Motion System Based on Chaos Immune Algorithm and RBF Network," paciia, vol. 2, pp.502-506, 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, 2008
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