Control chart pattern recognition using adaptive back-propagation artificial Neural networks and efficient features

Control chart patterns are important statistical process control tools for determining whether a process is running in its intended mode or in the presence of unnatural patterns. Accurate recognition of control chart patterns is essential for efficient system monitoring to maintain high-quality products. This paper introduces a novel hybrid intelligent system that composed of two major decision layers. The patterns divided into three binary groups using Statistical feature and Neural networks in the first layer. In the second layer, in each of groups, recognition is done using shape features and Neural networks. One of these features is novel in this area. In learning of neural networks, indifference of training algorithm due to parameter change has an important role in succession of an algorithm. Therefore adaptive back-propagation algorithm is applied for training of neural networks. Simulation results show that the proposed system has high recognition accuracy.