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

  • 摘要: 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.

     

    Abstract: 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.

     

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