摘要
综合运用近似模型参数优化技术和稳健性优化方法对汽车乘员约束系统进行优化。通过全局灵敏度分析,选出对加权伤害准则(weighted injury criterion,WIC)影响大的参数;采用粒子群优化(PSO)算法对支持向量回归(SVR)模型参数和核函数参数进行优化,建立高精度的PSO-SVR近似模型;在确定性优化的基础上进行基于蒙特卡罗抽样的稳健性优化。结果表明:优化后乘员约束系统性能得到明显提升且兼顾了稳健性。
By comprehensively utilizing the parameter optimization technique for approximate model and robust optimization method,vehicle occupant restraint system is optimized.The parameters having significant effects on weighted injury criterion(WIC)are selected by global sensitivity analysis.The parameters of support vector regression(SVR)model and kernel function are optimized by using particle swarm optimization(PSO)algorithm,and a high accuracy PSO-SVR approximation model is established.On the basis of deterministic optimization,robust optimization based on Monte Carlo sampling is also carried out.The results show that after optimization the performances of occupant restraint system are apparently enhanced with good robustness.
作者
张海洋
胡帅帅
周大永
高剑武
谷先广
Zhang Haiyang;Hu Shuaishuai;Zhou Dayong;Gao Jianwu;Gu Xianguang(Geely Automobile Research Institute, Zhejiang Key Laboratory of Automobile Safety Technology, Hangzhou 311228;Taihang Changqing Automobile Safety System (Suzhou) Co., Ltd., Suzhou 215100;School of Automobile and Transportation Engineering, Hefei University of Technology, Hefei 230009)
出处
《汽车工程》
EI
CSCD
北大核心
2020年第4期462-467,共6页
Automotive Engineering
基金
中国博士后基金(2018M640524)
浙江省汽车安全技术研究重点实验室开放基金(2009E10013)资助。
