适应智能质量控制的多分类支持向量机

Multi-class Support Vector Machine for Intelligent Quality Control

分析了现有控制图识别器在实际应用中存在的缺陷,并提出了一种基于支持向量机(SVM)的新方法.为了克服HAH多分类SVM(HAH-SVM)的缺陷,提高识别速度和准确率,设计了一种有针对性的SVM多分类器进行模式识别.仿真实验结果表明,该方法相对现有的BP和HAH-SVM方法能得到更高的识别率和识别速度,适合于工序的实时在线控制.

This paper analyzes the limitations of current control chart recognizers in practical applications, and presents a new method based on support vector machine （SVM）. In order to overcome the shortcomings of Half- Against-Half SVM （HAH-SVM） and improve the recognition speed and accuracy, a special multi-class SVM- recognizer is designed for pattern identification. Simulation and experimental results show that, compared with BP （backpropagation） and HAH-SVM methods, the presented method can obtain a faster recognition speed and a higher recognition accuracy, and can be applied to an online real time control process.