پاورپوینت کامل Control Chart Pattern Recognition Using an Optimized Neural Network and Efficient 49 اسلاید در PowerPoint

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a r t i c l e i n f o
 Article history:Received 1 November 2009Received in revised form۲۰ March 2010Accepted 24 March 2010Available online 18 April 2010
Keywords:
Control chart pattern recognitionWavelet decomposition entropiesNeural networksLearning algorithmParticle swarm optimization
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 eneralization 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.” ۲۰۱۰ ISA. Published by Elsevier Ltd. All rights reserved.
۱. Introduction Control chart patterns (CCPs) are important statistical process control tools for determining whether a process is run in its intended mode or in the presence of unnatural patterns. CCPs can exhibit six types of pattern: normal (NR), cyclic (CC), increasing trend (IT), decreasing trend (DT), upward shift (US) and downward shift (DS) [1]. All patterns other than normal patterns indicate that the process being monitored is not functioning correctly and requires adjustment