A hybrid intelligent technique for recognition of control chart patterns

Abstract

Control chart patterns (CCPs) are the important statistical process control tools for determining whether a process is run in its intended mode or in the presence of unnatural patterns. In this paper we proposed a hybrid intelligent technique for recognition of the CCPs. In this technique we have used a proper set of the shape features and statistical features as the efficient characteristics of the patterns. Then we proposed a hybrid heuristic recognition system based on particle swarm optimization (PSO) to improve the generalization performance of the radial basis function neural network (RBFNN) classifier. For this purpose, we have optimized the classifier design by searching for the best value of the parameters that tune its discriminate function. The obtained results show that the proposed technique has high recognition accuracy in comparison with other works. This recognition accuracy achieves with lower training samples.