Control Chart Pattern Recognition Using a Novel Efficient Feature and an Optimized RBF Neural Network

Control Chart Pattern Recognition Using a Novel Efficient Feature and an Optimized RBF Neural Network

Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in manufacturing processes. 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 feature extraction module and the classifier module. The feature extraction module uses the optimized capability features using the Bees Algorithm. This is applied for the first time in this area. In the classifier module several neural networks, such as the RBF, and GRNN, and PNN are investigated. Using an experimental study, we choose the best classifier in order to recognize the CCPs. Second, we propose a hybrid heuristic recognition system based on the Bees Algorithm to improve the performance of the classifier. Simulation results confirm that the proposed system outperforms other methods and shows high recognition accuracy about 100%.