Control chart pattern recognition using an optimized neural network and efficient features

Ebrahimzadeh, Ata

doi:10.1016/j.isatra.2010.03.007

Return

Control chart pattern recognition using an optimized neural network and efficient features

2010-07-01

PRE

Ebrahimzadeh, Ata *

DOI：10.1016/j.isatra.2010.03.007

Original

Original request for help

Save

Abstract

En 中文

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. (C) 2010 ISA. Published by Elsevier Ltd. All rights reserved.

Keywords:

Control chart pattern recognition

Wavelet decomposition entropies

Neural networks

Learning algorithm

Particle swarm optimization

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.