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                                       Details van artikel 101 van 129 gevonden artikelen
 
 
  Real-time recognition of control chart patterns in autocorrelated processes using a learning vector quantization network-based approach
 
 
Titel: Real-time recognition of control chart patterns in autocorrelated processes using a learning vector quantization network-based approach
Auteur: Guh, R. -S.
Verschenen in: International journal of production research
Paginering: Jaargang 46 (2008) nr. 14 pagina's 3959-3991
Jaar: 2008-07
Inhoud: Researchers have been investigating the use of artificial neural networks (NNs) in the application of control chart pattern (CCP) recognition with encouraging results in recent years. Most of the NN models in this field are designed to be used in uncorrelated processes where the process data are independent. Unfortunately, the prerequisite of data independence is not even approximately satisfied in many manufacturing processes. To the best of the author's knowledge, no research results have been published to date on the application of NNs for CCP recognition in autocorrelated processes. This work first shows that autocorrelation in process data greatly affects the performance of NN-based CCP recognizers developed with independent data and then presents a learning vector quantization network-based system that can effectively recognize CCPs in real-time for processes with various levels of autocorrelation. The system performance is evaluated in terms of the classification rate and the average run length. An empirical comparison using simulation indicates that the proposed learning-based system performs better than the traditional control chart methods in detecting shifts when the process data are positively correlated, while it also offers pattern classification. A demonstration example is provided using real data.
Uitgever: Taylor & Francis
Bronbestand: Elektronische Wetenschappelijke Tijdschriften
 
 

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