Principal component analysis for control chart pattern recognition using artificial neural networks

Control chart pattern (CCP) recognition is important for monitoring process environments to achieve appropriate control precisely and quickly and to produce high quality products. CCPs are represented by a large number of inputs. The principal component analysis (PCA) is an effective procedure for reducing a large input vector to a small vector. This paper describes an efficient approach to reducing the inputs of the networks for CCP recognition with the use of PCA. The reason for applying PCA to CCP recognition is to provide simplicity for the networks and to speed up the training procedure of them. Multilayered perceptrons (MLP) are used and trained with the resilient-propagation (RP) and the backpropagation (BP) learning algorithms. The results show that PCA provides less complex neural network structure for accurate and faster training. This helps to achieve the CCP recognition precisely and accurately and might even help us to implement the recognition easily within the VLSI technologies for this application