Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/17593
Title: Detection and identification of mean shifts in multivariate autocorrelated processes: A comparative study
Authors: WANG YU
Keywords: SPC, neural networks, the Hotelling T-square chart, the MWEMA chart, the Z chart
Issue Date: 18-Apr-2007
Source: WANG YU (2007-04-18). Detection and identification of mean shifts in multivariate autocorrelated processes: A comparative study. ScholarBank@NUS Repository.
Abstract: Complex processes with autocorrelated multivariate quality characteristics often exist in business/industry. However, limited researches have been done in detecting and identifying process mean shift in multivariate autocorrelated processes. In this thesis, a neural-network based control scheme is proposed to simultaneously detect and identify mean shifts in multivariate autocorrelated processes. The proposed control scheme utilizes the effective Extended Delta-Bar-Delta learning rule and is trained with the powerful Back-Propagation algorithm. To illustrate the power of the proposed control scheme, its Average Run Length (ARL) performance is evaluated against three statistical control charts, namely, the Hotelling T-square chart, the MEWMA chart, and the Z chart, in multivariate autocorrelated processes. It is shown that the NN-based control scheme can detect and identify mean shifts effectively and efficiently in multivariate autocorrelated processes.
URI: http://scholarbank.nus.edu.sg/handle/10635/17593
Appears in Collections:Master's Theses (Open)

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