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

Author

Shen, Yi ; Bu, Yunfeng ; Yuan, Mingxin

Author_Institution

Dept. of Mech. Eng., Huaiyin Inst. of Technol., Huaiyin

Volume

2

fYear

2008

fDate

19-20 Dec. 2008

Firstpage

502

Lastpage

506

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.

Keywords

artificial immune systems; automated highways; chaos; nonlinear systems; radial basis function networks; RBF network weights; WIM system; chaos disturbance; chaos immune algorithm; data processing; logistic equation; model detection precision; nonlinear system model; radial basis function; weigh-in-motion system; Chaos; Conferences; Immune system; Mechanical engineering; Neural networks; Radial basis function networks; Road transportation; Signal processing algorithms; Traffic control; Vehicle driving; RBF network; chaos immune algorithm; weigh-in-motion;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Industrial Application, 2008. PACIIA '08. Pacific-Asia Workshop on

Conference_Location

Wuhan

Print_ISBN

978-0-7695-3490-9

Type

conf

DOI

10.1109/PACIIA.2008.233

Filename

4756826