A Study on the Influence of Skewed Populations on Statistical and Neural Network Control Charts for Process Mean

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dc.contributor.authorYew Jin, Ooi
dc.date.accessioned2018-12-27T02:52:55Z
dc.date.available2018-12-27T02:52:55Z
dc.date.issued2009-05
dc.description.abstractShewhart control chart is an important statistical tool for detecting the presence of special cause n the process. It is widely used in different fields of manufacturing processes and it is based on he assumption that the means of samples which are drawn from the population in the process are 1ormally distributed. Some actions such as using Box-Cox power transformation and increasing he sample size are used when the normality assumption is not valid in practice. In this lissertation, the average run length (ARL) performance is compared among the neural network based control chart, Shewhart control chart, EWMA control chart and CUSUM control chart by Using the gamma distribution generated data in various skewnesses to measure the robustness of he control charts. Dev C++ is used to develop and analyze the data in neural network based :ontrol chart, while SAS is used to generate the gamma distributed data, develop and analyze the lata in Shewhart, EWMA and CUSUM control charts. From the results obtained, the neural Letwork based control chart is less robust compared to the statistical based control charts which ncludes Shewhart X control chart, EWMA and CUSUM based control charts. Based on these easons, we concluded that the EWMA based control chart is more robust to be applied when the kewness is high in the data.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/7394
dc.language.isoenen_US
dc.subjectSkewed populationsen_US
dc.subjectProcess meanen_US
dc.subjectNeural network controlen_US
dc.titleA Study on the Influence of Skewed Populations on Statistical and Neural Network Control Charts for Process Meanen_US
dc.typeThesisen_US
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