Empirically-based modelling approaches to the truck weigh-in-motion problem
Empirically-based modelling approaches to the truck weigh-in-motion problem
复制标题
基于经验的卡车动态称重问题建模方法
DOI：
10.1109/wsc.2015.7408491
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
I. Flood
中科院分区：
文献类型：
--
作者：
Yueren Wang;I. Flood
本文开发和比较了一个全面的配置的经验建模技术解决卡车分类的称重运动问题。回顾现有的人工神经网络的方法来解决这个问题，其次是与支持向量机的深入比较。三个主要的模型格式被认为是：（i）一个单一的结构与一个对所有的策略，用于选择卡车类型;（ii）一个数组的子模型，每个专用于一个卡车类型与一个对所有的卡车类型选择策略;和（iii）一个数组的子模型，每个专用于选择对之间的卡车。总体而言，SVM方法被发现优于基于ANN的模型。本文最后提出了一些建议，以便将工作扩展到更广泛的问题范围。
The paper develops and compares a comprehensive range of configurations of empirical modeling techniques for solving the truck classification by weigh-in-motion problem. A review of existing artificial neural network approaches to the problem is followed by an in-depth comparison with support vector machines. Three main model formats are considered: (i) a monolithic structure with a one versus all strategy for selecting truck type; (ii) an array of sub-models each dedicated to one truck type with a one versus all truck type selection strategy; and (iii) an array of sub-models each dedicated to selecting between pairs of trucks. Overall, the SVM approach was found to outperform the ANN based models. The paper concludes with some suggestions for extending the work to a broader scope of problems.