RBF网络在基于动载的路面识别中的应用  

Application of RBF Networks in Road Recognition Based on Dynamic Load

作　　者：李忠国[1] 张为公[2] 王琪[1] 李世民 

机构地区：[1]江苏科技大学机械工程学院,江苏镇江212003 [2]东南大学仪器科学与工程系,南京210096 [3]潍坊恒安散热器集团有限公司,山东潍坊262100

出　　处：《重庆工学院学报（自然科学版）》2009年第1期1-5,共5页Journal of Chongqing Institute of Technology

基　　金：江苏省交通厅资助项目(05C02);江苏科技大学博士启动基金

摘　　要：设计了基于车轮垂直动载的路面识别的RBF网络分类器.对正则化、网络集成、添加噪声及最优停止4种神经网络泛化方法进行了归纳分析.输入加入强度为0.025的噪声,并用5个子网进行集成处理后,测试样本正确识别率最高,达50%.上述方法结合最优停止法时,测试正确率没有提高反而下降.反映了RBF网络在小样本识别中泛化处理的某些特点.测试结果表明:适当泛化后的RBF神经网络可用于基于垂直动载的路面识别.RBF networks classifier employed in road recognition based on wheel vertical dynamic load （VDL） is designed. Four generalization methods, including normalization method, net integration method, adding noise method and best time stop method, are summarized and analyzed. The highest recognition rate of test samples is 50 % when noise with power of 0. 025 is added in input data and 5 neLs are integrated. When above method combined with best time stop method, recognition rate doesn＇t increase but declines instead. Some generalization law of RBF networks in small sample recognition is revealed. Test results indicate RBF networks can be utilized in road recognition based on VDL.

关 键 词：RBF网络 分类 泛化分析 路面不平度 

分 类 号：U463[机械工程—车辆工程]