A Neura l Network Approach to Synthetic Control Chart for the Process Mean

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Date
2010-09-30
Authors
Michael, Khoo Boon Chong
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Universiti Sains Malaysia
Abstract
(An abstract of between 100 and 200 words must be prepared in Bahasa Malaysia and in English. This abstract will be included in the Annual Report of the Research and Innovation Section at a later date as a means of presenting the project findings of the researcher/s to the University and the community at large) In this project, a multivariate synthetic control chart for monitoring the process mean vector of skewed populations using weighted standard deviations has been proposed. The proposed chart incorporates the weighted standard deviation (WSD) method of Chang and Bai (2004) into the standard multivariate synthetic chart of Ghute and Shirke (2008). The standard multivariate synthetic chart consists of the Hotelling's f2 chart and the conforming run length (CRL) chart. The results show that the proposed chart performs better than the existing multivariate charts for skewed populations, in terms of false alarm rates and moderate and large mean shift detection rates based on the various degrees of skewnesses. This project also suggest a synthetic double sampling (DS) chart for the mean. The synthetic DS chart comprises a DS sub-chart and a CRL sub-chart. The DS chaIt was proposed by Daudin (1992) while the CRL chaIt was suggested by Bourke (1991). The synthetic DS chart outperforms both of its standard counterparts, namely the synthetic chart of Wu and Spedding (2000) and the DS chart of Daudin (1992). In addition, a comparison between statistical charts and neural network based charts is made, where the results indicate that neural network based charts perform better.
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