摘要
器材合理分类是建立预测模型的基础,某型舰船仪表器材数据较少、分类指标因素不足,使用传统方法易产生过拟合的问题;提出蒙特卡洛K-means算法,利用样本方差进行器材消耗聚类分析;该方法首先利用MC法计算初始聚类中心,参考SBC分类法制定聚类种类数k,通过方差聚类建模来优化仪表器材的分类,最终得到仪表器材的聚类结果;实例计算表明,该方法能够有效改进K-means方法的分类结果,无需考虑其他备件指标因素影响,适用于数据量过小和存在白噪声的模型。
The reasonable classification of spare-parts is the basis of establishing the prediction model,the data of a certain warship spare-parts is less and the classification index factor is insufficient,so it is easy to use the traditional method to produce the problem of overfitting.A Monte Carlo K-means algorithm is proposed,and the sample variance is used for the spare-parts consumption volatility cluster analysis.Firstly,using Monte Carlo to calculate the initial clustering center,and refers to the SBC method to formulate the number of clustering categories k.The classification of instrument spare-parts is optimized by variance clustering modeling,and finally the cluster results of instrument spare-parts are obtained.The example shows that the method can effectively improve the classification results of K-means method without considering other index factors.It is suitable for the model with too small amount of data and white noise.
作者
吴雯雯
陈振林
Wu Wenwen;Chen Zhenlin(Naval Aviation University Coast Defence Academy,Yantai 264001,China)
出处
《计算机测量与控制》
2020年第4期222-226,共5页
Computer Measurement &Control
