Application of the PSO-RBFNN Model for Recognition of Control Chart Patterns

Control chart patterns (CCPs) are important statistical process control tools for determining whether a process is run in its intended mode or in the presence of unnatural patterns. This study investigates the design of an accurate system for control chart pattern (CCP) recognition from two aspects. First, an efficient system is introduced that includes two main modules: the clustering module and the classifier module. In the clustering module, the input data will be clustered by fuzzy C-mean (FCM) clustering method. Then the Euclidean distance of each pattern is computed from the determined clusters. The classifier module determines the membership of the patterns using the computed distance. In this module, several neural networks, such as the multilayer perceptron and radial basis function are investigated. Second, we propose a hybrid heuristic recognition system based on particle swarm optimization (PSO) algorithm to improve the generalization performance of the classifier. Simulation results show high recognition accuracy for the proposed system.