|
|
|
| Improved Pattern Clustering Algorithm and its Application in Recognition of Transversal Thickness Distribution of Steel Strip |
| Tang Cheng-Long Shi-gang WANG Qin-hua Liang Wei Xu |
|
|
|
|
Abstract Transversal thickness distribution of steel strip in the entry section of cold rolling mill has distinct affections to the flatness and transversal thickness control precision of final products. Pattern clustering method is introduced to steel rolling area and is first time to be used in the patterns recognition of transversal thickness distribution of steel strip. K-means clustering algorithm as the best-known one has the advantage of being easy to implement, it has drawbacks. In this paper, an improved k-means clustering algorithm is presented, main improvement points include the amount of clusters is indirectly determined by experience, the initial clustering points are preselected according to the density queue of data objects and Mahalanobis distance is applied instead of Euclidean distance. Compared to the clustered patterns obtained from the common k-means algorithm, the patterns identified from the improved algorithm is more reasonable. The results of application in one coil further show the improved clustering algorithm is well suitable for the patterns�� recognition of transversal thickness distribution of steel strip. It will do great help in the online quality control system.
|
|
Received: 31 March 2008
|
|
Corresponding Authors:
�Ƴ��� Tang Cheng-Long
|
|
|
|
|
|
|
2009, Vol. 16 
