Control chart pattern recognition using an optimized neural network and efficient features.
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 entropies of the wavelet packets. These are applied for the first time in this area. In the classifier module several neural networks, such as the multilayer perceptron and radial basis function, 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 particle swarm optimization to improve the generalization performance of the classifier. The results obtained clearly confirm that further improvements in terms of recognition accuracy can be achieved by the proposed recognition system.
Ebrahimzadeh
Ata
A
Faculty of Electrical and Computer Engineering, Babol University of Technology, Babol 4213743556, Iran. ataebrahim@yahoo.com
Ranaee
Vahid
V
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D019277:Entropy; D007221:Industry / Q000706:statistics & numerical data*; D016571:Neural Networks, Computer; D052638:Particulate Matter; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results
D052638:Particulate Matter