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Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/16494

Title: An Expert System Using RBF Neural Network for Estimating Vehicle Speed Based on Length of Skid Mark
Authors: Tseng, Wen-Kung;Liao, Shih-Syong
Contributors: 車輛科技研究所
Keywords: ABS;An expert system;Neural network;Radial basis function;Skid mark
Date: 2011
Issue Date: 2013-05-06T04:45:04Z
Publisher: IEEE
Abstract: This paper presents an expert system to estimate the relationship between the vehicle pre-braking speed and the length of the skid mark. Since the length of the skid mark varies with many factors, there is no a single formula or equation which can represent the relationship between the vehicle pre-braking speed and the length of the skid mark. Therefore in this paper an expert system is built to estimate the relationship between the vehicle pre-braking speed and the length of the skid mark. The radial basis function (RBF) neural network is used for the expert system due to its shorter training time and higher accuracy. There are many factors affecting the skid mark. In this paper we choose 7 factors, i.e. brand of vehicle, vehicle displacement, year of manufacture, vehicle weight, vehicles with and without ABS, roadway surface, and vehicle speed for the training in the RBF neural network. The total number of the training data for the RBF neural network is 2619. The results showed that high accuracy is obtained for estimating the relationship between the vehicle pre-braking speed and the length of the skid mark. Thus the expert system proposed in this paper is demonstrated to be a suitable system for estimating the relationship between the vehicle pre-braking speed and the length of the skid mark.
Relation: Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011, 2: 631-635
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