CAJ | 학술논문

带钢的表面缺陷模式识别是一个生产当中遇到的多分类问题。传统分类器面对多分类问题往往出现泛化性能差，过度学习，识别率低等问题。本文采用遗传算法（GA）对支持向量机（SVM）分类器进行参数优化，并将其应用于带钢表面缺陷分类的模式分类。实验数据来源于UCI标准数据集，采用10折交叉验证法进行分类仿真。通过与传统SVM分类器以及BP神经网络等方法进行对比，GA-SVM模型的分类准确率更高，对带钢表面缺陷多分类问题有一定指导作用。
대강적표면결함모식식별시일개생산당중우도적다분류문제。전통분류기면대다분류문제왕왕출현범화성능차，과도학습，식별솔저등문제。본문채용유전산법（GA）대지지향량궤（SVM）분류기진행삼수우화，병장기응용우대강표면결함분류적모식분류。실험수거래원우UCI표준수거집，채용10절교차험증법진행분류방진。통과여전통SVM분류기이급BP신경망락등방법진행대비，GA-SVM모형적분류준학솔경고，대대강표면결함다분류문제유일정지도작용。
Strip surface defect classification is a multi-classification problem encountered in the production. Traditional classifiers are often faced with the problem like poor generalization performance, excessive learning and low accuracy. In this paper, SVM model optimized by GA was implemented for systematical research of strip surface defect classification problem. In the paper, UCI database was implanted as experimental data, and 10-fold cross-validation was used for simulation. The result showed that GA-SVM model worked better on accuracy than traditional SVM, BP neural networks and some other methods, which had a guiding role on strip surface defect classification.