EMD和LS-SVM相结合的高压水射流靶物材质识别方法研究  

Target material identification for high pressure water-jet based on EMD and LS-SVM

作　　者：杨洪涛[1] 孙帅[1] 方传智 牛明强[1] 

机构地区：[1]安徽理工大学机械工程学院,淮南232001

出　　处：《电子测量与仪器学报》2014年第10期1074-1083,共10页Journal of Electronic Measurement and Instrumentation

基　　金：国家自然科学基金(61272540)资助项目

摘　　要：为了利用靶物反射声有效识别靶物材质,提出了一种新的基于经验模态分解(EMD)和最小二乘支持向量机(LSSVM)相结合的靶物材质识别方法。首先应用模极大值算法识别靶物边界点,提取边界点内的反射声信号进行小波降噪预处理,对预处理后的信号进行EMD分解,并计算获得各个本证模态(IMF)分量的短时能量比,作为对应不同靶物材质的特征值输入到利用LS-SVM建立的多分类模型。介绍了上述方法的基本原理,设计了试验装置和靶物材质识别影响因素分析试验方案。实验结果表明:靶物的内部结构和外形大小因素对靶物材质识别率影响小,利用上述方法进行的四种靶物材质探测,平均识别率达到85.83%,比BP神经网络提高了18.83%,且运算速度也得以提高,因此该方法可以用于靶物材质的识别。In order to identify target material effectively by using the reflective sound of target ,a new identification method of the target material based on empirical mode decomposition （ EMD） and least squares support vector ma-chine （LS-SVM） is presented.The signal border indicating where the target locates was identified by using modulus maxima algorithm .The reflective sound signal within the border was extracted and was processed by applying wave-let noise reduction to reduce the noise .The pretreated reflective sound signal was decomposed by using EMD meth-od and the short-time energy ratio of every decomposed IMF was calculated ,which was considered as the eigenval-ues of target and was input into target identification model .The basic principle of the method was described .The ex-perimental equipment was built and the influence factor analysis experiments and the comparative experiments of target identification rate were done .The experimental results show that there is little effect on the target material identification rate.The average target material identification rate can reach 85.83%by using the built target materi-al identification model.There is a increase of 18.83% compared with BP-NN method,and the calculating speed is also improved.So the built target material identification model can be used for the target material identification .

关 键 词：靶物材质识别 反射声信号 经验模态分解 本征模态函数 短时能量比 最小二乘支持向量机 

分 类 号：TN912.34[电子电信—通信与信息系统] TP69[电子电信—信息与通信工程]