适应智能质量控制的多分类支持向量机

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．

Multi-class Support Vector Machine for Intelligent Quality Control

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.