安徽农业大学学报2017，Vol.44Issue(1)：153-157,5.DOI:10.13610/j.cnki.1672-352x.20170208.014
基于改进AFSA的路面不平度时域估测
Time domain estimation of road roughness with improved AFSA
摘要
Abstract
In order to improve the accuracy of the time domain model based on vehicle dynamic response,the design of the RBF neural network,the dynamic response parameters of the neural network and the position of the vehicle body measuring points were conducted.Based on the Lagrange second equation,degree 5 of freedom vibration model of vehicle body at any position was established.The road time domain excitation was established for input of vehicle excitation and ideal output of neural network by the method of filtering white noise.An improved artificial fish swarm algorithm (AFSA) was used to establish an optimization model for vehicle body measurement point centroid distance,the type of dynamic response parameters to be measured,and expansion coefficient of RBF neural network.Through the research,a strategy with two kinds of vehicle dynamic response parameters was proposed,as well as the specific location of the vehicle body measurement points.The results showed that the accuracy of the two schemes is very high,and the accuracy of the time domain is higher than 0.99.关键词
路面不平度/时域模型/RBF神经网络/人工鱼群算法Key words
road roughness/time domain model/RBF neural network/artificial fish swarm algorithm分类
交通工程引用本文复制引用
王静,鲁杨,程准,刘奕贯,鲁植雄..基于改进AFSA的路面不平度时域估测[J].安徽农业大学学报,2017,44(1):153-157,5.基金项目
国家自然科学基金项目(51175269)资助. （51175269）
