|本期目录/Table of Contents|

[1]刘金燕a,王冬青b,崔建伟a.基于改进核LS-SVM算法的螺丝锁附结果分类研究[J].工业仪表与自动化装置,2020,(04):12-15.[doi:1000-0682(2020)04-0000-00]
　LIU Jinyana,WANG Dongqingb,CUI Jianweia.Research on classification of screw locking results based on improved kernel LS-SVM algorithm[J].Industrial Instrumentation & Automation,2020,(04):12-15.[doi:1000-0682(2020)04-0000-00]
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基于改进核LS-SVM算法的螺丝锁附结果分类研究

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2020年04期
页码:
12-15
栏目:
出版日期:
2020-08-15

文章信息/Info

Title:
Research on classification of screw locking results based on improved kernel LS-SVM algorithm
作者:
刘金燕a; 王冬青b; 崔建伟a
青岛大学 a. 自动化学院；b. 电气工程学院，山东 青岛 266071
Author(s):
LIU Jinyana;  WANG Dongqingb;  CUI Jianweia
a. School of Automation Engineering; b. School of Electrical Engineering, Qingdao University, Qingdao 266071, China
关键词:
关键词：螺丝锁附; LS-SVM; 分类; 泰勒展开; 参数选取
Keywords:
screw locking;  LS-SVM;  classification;  Taylor expansion;  parameter selection
分类号:
TP391.4
DOI:
1000-0682(2020)04-0000-00
文献标志码:
A
摘要:
该文将自动分类技术应用于手机螺丝锁附的结果分类中，由此提出了一种改进的最小二乘支持向量机算法(least squares support vector machine,LS-SVM)。一方面，通过在径向基函数上进行泰勒展开，并选择前3项改进目标函数减少计算量；另一方面，在参数选取时考虑计算速度因子，以提高计算速度。仿真结果表明，改进后的LS-SVM算法与传统的LS-SVM算法具有相同的准确率，但运算速度更快，具有更强的实用性。
Abstract:
The article aims to apply the automatic classification technology to the classification of the results attached to the mobile phone screw locking,so an improved least square support vector machine(LS-SVM) algorithm is proposed. On the one hand,Taylor expansion is performed on the radial basis function,and take the first three items to reduce the calculation amount.On the other hand,the calculation speed factor is considered in the parameter selection to increase the calculation speed.The simulation results show that the improved LS-SVM algorithm has the same accuracy as the traditional LS-SVM algorithm,but the operation speed is faster,so it’s more practical.

参考文献/References:

[1] Dhayagude N, Gao ZQ, Mrad F. Fuzzy logic control of automated screw fastening[J].Robotics and Computer Integrated Manufacturing:An International Journal of Manufacturing and Product and Process Development, 1996, 12 (3): 235-242.

[2] Ogawa S, Shimono T, Kawamura A, Nozaki T. Position control in normal direction for the fast screw-tightening[J]. Institute of Electrical and Electronics Engineers Inc, 2016,1(1):3429-3433.[3] Aronson RM, Bhatia A, Jia ZZ, et al. Data-Driven Classifi- cation of screwdriving operations[J].International Sympo- sium on Experimental Robotics,2016,10(12):1120-1130.?div>[4] 成亚玲,谭爱平.一种基于改进最小二乘支持向量机的变压器故障诊断方法研究[J].制造业自动化,2015(20):87-89.
[5] 王普,辛娇娇,高学金,等.基于独立元分析-最小二乘支持向量机的冷水机组故障诊断方法[J].北京工业大学学报, 2017, 43 (11): 1641-1647.
[6] 徐可,陈宗海,张陈斌,等.基于经验模态分解和支持向量机的滚动轴承故障诊断[J].控制理论与应用,2019,36(6): 915-922.
[7] 李航.统计学习方法[M].北京:清华大学出版社,2012: 102-106.
[8] 刘立峰,武奇生,姚博彬.基于高斯尺度空间和SVM的桥梁裂缝检测研究[J].工业仪表与自动化装置,2019(1): 13-16+114.
[9] 杜青青.基于最小二乘支持向量机逆系统方法应用研究[J].工业仪表与自动化装置,2019(5):122-124.

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备注/Memo

备注/Memo:
收稿日期：2019-12-16
基金项目：
国家自然科学基金项目(61873138；1573205)
作者简介：
刘金燕(1996)，女，山东青岛人，硕士研究生，主要研究方向为模式识别、机器学习。
更新日期/Last Update: 1900-01-01