【摘要】 带钢表面缺陷分类是一个多分类问题,采用传统分类器进行分类识别率较低.建立PSO优化SVM模型,对带钢表面分类问题进行了系统研究.模型采用交叉验证法,针对UCI数据库的带钢表面缺陷生产数据进行了实例分析,该方法可对1941组生产数据中的7种不同类型缺陷进行较为准确的分类.经过与传统SVM、BP神经网络等方法进行对比,PSO-SVM体现出了更高的准确性和泛化能力,对生产实际有一定的指导作用.更多还原

【Abstract】 Strip surface defect classification is a multi-classification problem,traditional classifiers are faced with low accuracy on this problem.SVM model optimized by PSO is implemented for systematical research of strip surface defect classification problem.In the model,K-CV statistical analysis is used and the production data provided by UCI database is analyzed.The result shows that by using the PSO-SVM model,the whole 1941 sets of data with 7different kinds of defects have been classified pretty accurately.Compared with traditional SVM,BP neural networks and some other methods,PSO-SVM shows a higher accuracy and better generalization ability,which plays a guiding role on actual production.更多还原