NDLI: Study on Weigh-in-Motion System Based on Chaos Immune Algorithm and RBF Network

Content Provider	IEEE Xplore Digital Library
Author	Yi Shen Yunfeng Bu Mingxin Yuan
Copyright Year	2008
Description	Author affiliation: Sch. of Mech. Eng., Xian Jiaotong Univ.., Xian (Mingxin Yuan) \|\| Dept. of Mech. Eng., Huaiyin Inst. of Technol., Huaiyin (Yi Shen; Yunfeng Bu)
Abstract	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.
Starting Page	502
Ending Page	506
File Size	605896
Page Count	5
File Format	PDF
ISBN	9780769534909
DOI	10.1109/PACIIA.2008.233
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Chaos chaos immune algorithm Conferences Vehicle driving weigh-in-motion RBF network Mechanical engineering Road transportation Neural networks Signal processing algorithms Radial basis function networks Traffic control Immune system
Content Type	Text
Resource Type	Article

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