Defect Recognition of Cold Rolled Plate Shape Based on RBF-BP Neural Network

被引：0

作者：

Li, Xiaohua ^{[1
]}

Zhang, Junjie ^{[1
]}

机构：

[1] Univ Sci & Technol Liaoning, Inst Elect & Informat Engn, Anshan, Liaoning, Peoples R China

来源：

PROCEEDINGS OF THE 10TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA 2012) | 2012年

关键词：

plate shape defects; combinational RBF-BP neural network; pattern recognition;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

By means of the analysis for the defect pattern of plate shape, a shape defect recognition method for cold rolled strips is proposed based on RBF-BP neural network in this paper. The memberships relative to six basic patterns of common plate shape defects are identified. This method syncretizes the advantages of RBF and BP neural network. There are very fast approaching speed and high precision of network recognition. The simulation of the proposed method is done, and the simulation results are compared with the results of the recognition method by using BP neural network. The results show that the recognition method proposed in this paper gives better effect than the one making use of single network. And it is more suitable for real-time shape control.

引用

页码：496 / 500

页数：5