Weigh-in-motion based on multi-sensor and RBF neural network

Author

Zhaojing, Tong ; Xiuhua, Shi ; Qunpo, Liu ; Dahu, Wang

Author_Institution

Coll. of Marine, Northwestern Polytech. Univ., Xi´´an, China

fYear

2011

fDate

15-17 April 2011

Firstpage

944

Lastpage

947

Abstract

The work presented in this paper focuses on several aspects of weigh-in-motion. According to the mathematical model of weigh-in-motion, this paper proposed the methods of multi sensor data acquisition and axle weight detection by using quadratic mean. The Radial Basis Function (RBF) neural network was used to construct the weighing system. In the modeling and training of RBF neural network, three different types of test were given: dead load, normal load and overweight. The results have indicated that using RBF neural network in weigh-in-motion has a significant effect on weighing test precision.

Keywords

axles; data acquisition; learning (artificial intelligence); radial basis function networks; road traffic; sensor fusion; statistical analysis; RBF neural network; axle weight detection; dead load test; mathematical model; multisensor data acquisition; normal load test; overweight test; quadratic mean; radial basis function neural network training; weigh-in-motion aspect; weighing system; weighing test precision; Artificial neural networks; Axles; Mathematical model; Neurons; Testing; Training; Vehicles; Quadratic Mean; RBF Neural Network; Weigh-in-Motion;

fLanguage

English

Publisher

ieee

Conference_Titel

Electric Information and Control Engineering (ICEICE), 2011 International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-8036-4

Type

conf

DOI

10.1109/ICEICE.2011.5777986

Filename

5777986