一种SOM和K-Means结合算法在SMT焊接质量中的应用研究  

Application of A SOM and K-Means Algorithm in Welding Quality in SMT

作　　者：张强武[1] 唐露新[1] 

机构地区：[1]广东工业大学信息工程学院,广州510006

出　　处：《计算机与数字工程》2014年第7期1127-1130,1136,共5页Computer & Digital Engineering

基　　金：广东省教育部产学研合作专项资金(编号:2012B091100056)资助

摘　　要：针对SOM神经网络算法复杂度高精度低以及K-Means聚类算法需事先确定聚类(簇)数目和随机选取初始聚类中心的不足,论文提出了一种SOM神经网络与K-Means相结合的S-K二次聚类算法,进行功能互补。该算法应用在SMT焊接质量上,能提高数据聚类信息的精确度,直观地看到数据的分布情况,改善系统的整体性能。As the high complexity and low precision of the SOM neural network algorithm and the shortcomings of the K-Means clustering algorithm needs to determine the number of clusters advanced and randomly select initial clustering cen-ters ,a SOM neural network combined with K-Means of S-K secondary clustering algorithm is proposed ,having complemen-tary function .If the algorithm is used in SMT soldering ,the precision of data clustering information can be enhanced .The intuitive vision of data distributioncould be got and the overall performance of the system is improved .

关 键 词：SOM神经网络 K-Means类聚 SMT 焊接质量 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]