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.