基于支持向量机的陶瓷原料成分识别  

Ldentification of Ceramic Raw Material Components Based on Support Vector Machine

作　　者：郭若楠 Guo Ruonan(Xi'an Shiyou University,Xi'an,China)

机构地区：[1]西安石油大学,陕西西安

出　　处：《科学技术创新》2024年第2期32-35,共4页Scientific and Technological Innovation

摘　　要：针对科学合理的分类陶瓷原料问题,提出了基于支持向量机的陶瓷原料成分识别。首先对SVM理论进行了详细介绍,并构建SVM线性核分类器和SVM高斯核分类器;然后在此基础上开展陶瓷原料分类实验,对陶瓷原料分类实验结果进行对比总结。实验表明,线性核支持向量机预测准确率为96.7%,有更强的可行性和很好的鲁棒性。Aiming at the problem of scientific and reasonable classification of ceramic raw materials,the identification of ceramic raw materials based on support vector machine was proposed.Firstly,SVM theory is introduced in detail,and SVM linear kernel classifier and SVM Gaussian kernel classifier were constructed;Then,Then on this basis,the experiment of ceramic raw material classification is carried out,and the experimental results of ceramic raw material classification are compared and summarized.The experiment shows that the prediction accuracy of linear kernel support vector machine is 96.7%,which has stronger feasibility and good robustness.

关 键 词：SVM 陶瓷原料 成分识别 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] J527[自动化与计算机技术—控制科学与工程]