Improved Pattern Clustering Algorithm for Recognizing Transversal Distribution of Steel Strip Thickness  

作　　者：TANG Cheng-long WANG Shi-gang LIANG Qin-hua XU Wei 

机构地区：[1]School of Mechanical and Dynamical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

出　　处：《Journal of Iron and Steel Research International》2009年第5期50-55,共6页

基　　金：Sponsored by National Natural Science Foundation of China(50705057)

摘　　要：Transversal distribution of the steel strip thickness in the entry section of the cold rolling mill seriously affects to the flatness and transversal thickness precision of the final products. Pattern clustering method is introduced into the steel rolling field and used in the patterns recognition of transversal distribution of the steel strip thickness. The well-known k-means clustering algorithm has the advantage of being easily completed, but still has some drawbacks. An improved k-means clustering algorithm is presented, and the main improvements include： （1） the initial clustering points are preselected according to the density queue of data objects; and （2） Mahalanobis distance is applied instead of Euclidean distance in the actual application. Compared to the patterns obtained from the common kmeans algorithm, the patterns identified by the improved algorithm show that the improved clustering algorithm is well suitable for the patterns＇ recognition of transversal distribution of steel strip thickness and it will be useful in online quality control system.Transversal distribution of the steel strip thickness in the entry section of the cold rolling mill seriously affects to the flatness and transversal thickness precision of the final products. Pattern clustering method is introduced into the steel rolling field and used in the patterns recognition of transversal distribution of the steel strip thickness. The well-known k-means clustering algorithm has the advantage of being easily completed, but still has some drawbacks. An improved k-means clustering algorithm is presented, and the main improvements include： （1） the initial clustering points are preselected according to the density queue of data objects; and （2） Mahalanobis distance is applied instead of Euclidean distance in the actual application. Compared to the patterns obtained from the common kmeans algorithm, the patterns identified by the improved algorithm show that the improved clustering algorithm is well suitable for the patterns＇ recognition of transversal distribution of steel strip thickness and it will be useful in online quality control system.

关 键 词：transversal thickness distribution pattern recognition improved k-means algorithm density queue 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TG333.71[自动化与计算机技术—计算机科学与技术]