摘要
采用蜻蜓算法(DA)和最小二乘支持向量机(LSSVM)的方法,解决生产过程中小批量产品在质量预测方面的问题。首先以汽车变速箱轴承内圈孔直径的尺寸作为预测数据,连续观测12个单位时间,并记录每个单位时间轴承内圈孔直径的尺寸数据,进行归一化处理;其次采用LSSVM对变速箱轴承内圈孔直径加工过程变化进行量化分析,并采用蜻蜓算法优化LSSVM参数;最后将DA-LSSVM综合方法与多种预测模型进行对比分析。结果表明,DA-LSSVM方法可以提高预测模型的训练预测精度,缩短训练时间。
In this paper, the Dragonfly Algorithm(DA) and the least Squares support vector machine(LSSVM) are used to solve the problem of quality prediction of small batch products. Firstly, the size of inner ring hole diameter of automobile gearbox bearing is used as the forecast data, which has been continuously observed for 12 unit time. Dimensional data of the inner ring hole diameter of each unit time has been recorded for normalization treatment. Secondly, LSSVM is used to quantitatively analyze the change of the diameter of the inner ring hole of gearbox bearing, and the LSSVM parameter is optimized by Dragonfly algorithm. Finally, the DA-LSSVM synthesis method is compared with many kinds of forecasting models. The results show that the DA-LSSVM method can improve the precision of prediction model and shorten the training time.
作者
董海
徐德珉
Dong Hai;Xu Demin(School of Applied Technology,Shenyang University,Shenyang 110044,China;School of Mechanical Engineering,Shenyang University,Shenyang 110044,China)
出处
《科技管理研究》
CSSCI
北大核心
2019年第22期256-260,共5页
Science and Technology Management Research
基金
国家自然科学基金项目“具有柔性分组决策的吊机集成调度优化理论与方法研究”(71672117)
辽宁省自然科学基金项目“复杂机械产品协间设计过程建模优化与控制”(201602514)资助
