基于PSO-LSSVM的复杂试验不确定度分析  

Uncertainty analysis for complex test based on PSO-LSSVM

作　　者：洪谭亮 丁晓红[1] 王海华 王神龙 徐世鹏 HONG Tanliang;DING Xiaohong;WANG Haihua;WANG Shenlong;XU Shipeng(School of Mechanical Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;Yanfeng Andaotuo Seat Co.,Ltd.,Shanghai 201315,China)

机构地区：[1]上海理工大学机械工程学院,上海200093 [2]延锋安道拓座椅有限公司,上海201315

出　　处：《上海理工大学学报》2021年第1期29-34,共6页Journal of University of Shanghai For Science and Technology

摘　　要：提出一种结合粒子群优化(PSO)算法和最小二乘支持向量机(LSSVM)模型的复杂试验不确定度分析方法。以汽车座椅的安全带拉伸试验为对象,研究汽车座椅安全带拉伸试验的主要影响因素及其概率密度函数参数,采用拉丁超立方方法进行试验设计,并进行安全带拉伸试验有限元仿真。运用PSO-LSSVM建立安全带拉伸试验的数学模型,并与BP(back propagation)神经网络建立的数学模型进行对比,结果显示PSO-LSSVM数学模型有更高的预测精度,满足后续不确定度评定要求。进一步采用蒙特卡罗方法实现安全带拉伸试验的不确定度评定,并以国家标准规定的方法进行参考,研究结果表明,该方法可应用于各种复杂试验不确定度分析中。A complex experimental uncertainty analysis method combining particle swarm optimization(PSO)and least square support vector machine(LSSVM)model was presented.Taking the tensile test of the automobile seat belt as the object,the main influencing factors and probability density function parameters of the tensile test of automobile seat belt were studied.The Latin hypercube method was used to design the test,and the finite element simulation of the tensile test of the seat belt was carried out.PSO-LSSVM was used to establish the mathematical model of the safety belt tension test,and compared with the mathematical model established by back propagation(BP)neural network.The results show that the PSO-LSSVM mathematical model hashigher prediction accuracy and mets the requirements of the subsequent uncertainty evaluation.The Monte Carlo method was further used to evaluate the uncertainty of the safety belt tensile test,and the method was compared with the method specified in the national standard.The results show that the proposed method can be applied to the uncertainty analysis of complex experiments.

关 键 词：试验不确定度分析 粒子群优化 最小二乘支持向量机 蒙特卡罗法 

分 类 号：TG707[金属学及工艺—刀具与模具] TB9[一般工业技术—计量学]