Recognition of Control Chart Pattern by Using Adaptive Mutation  Particle Swarm Optimization and Support Vector Machine

Due to the complexity of production processes resulting from multi-item production, effective production control is necessary. For this purpose, an intelligent control chart pattern recognition method is proposed. This method can improve the recognition accuracy by using adaptive mutation particle swarm optimization (AMPSO) and support vector machine (SVM) classifier. It uses one-against-one SVM multi-class classifier to recognize the control patterns because of its excellent small sample learning. Meanwhile, AMPSO is used to optimize the parameters of SVM kernel function. 20-dimension simulated data sets of ten control chart patterns, including six fundamental patterns and four mix patterns, are used to test the proposed method. Also, it is compared with BP, SVM, and PSO-SVM methods. Simulation results show that the proposed method can get high recognition accuracy, which is up to 98.14%, while it is 75% if BP is applied. This implies that it is a feasible way to recognize control chart pattern in practice.