Control chart patterns recognition based on support vector machine
基于支持向量机的控制图模式识别

To improve patterns recognition performance of control chart,a pattern-recognition method based on hybrid kernel was presented.In the structure modeling,the one-against-one-algorithm multi-class classification Support Vector Machine(SVM) was applied,and genetic algorithm was used to optimize the parameters of SVM.The simulation results and application show that the performance of SVM with hybrid kernel is superior to single common kernel,probabilistic neural network and Wavelet Probabilistic Neural Network(WPNN) in the aggregate classification rate and type I error.What's more,it has simpler structure and quicker convergence that it could be applied in the on-line process quality control.