PCA-SVM for control chart recognition of genetic optimization

Considering the problem that the precision and generalization are not ideal when recognize the basic patterns of quality control chart in PCA and PCA-SVM modeling,this paper proposed a control chart pattern recognition method based on genetic algorithm and PCA-SVM.The basic idea of the method was that,firstly,in view of the dimensionality reduction in feature space,used principal component analysis algorithm to lower the sample dimension,it also highlighted the clustering features.Then regarded the component characteristics as a chromosome which was then performed with binary code.It used a support vector machine classifier to recognized a random chromosome and considered recognition accuracy as the fitness function to evaluate the fitness of individual feature.By the operations of selection,crossover and mutation,with GA self-adaptive optimizing for penalty parameter and kernel parameter.Finally,it introduced the optimized SVM modeling to identify the control chart pattern.The simulation experimental results demonstrate that the proposed method has higher detection accuracy and stronger generalization ability than other methods,so it is more suitable for quality control in production field.