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Discrete Dynamics in Nature and Society
Volume 2015 (2015), Article ID 892740, 7 pages
Research Article

Change Point Determination for an Attribute Process Using an Artificial Neural Network-Based Approach

Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City 24205, Taiwan

Received 28 January 2015; Revised 6 May 2015; Accepted 6 May 2015

Academic Editor: Carlo Piccardi

Copyright © 2015 Yuehjen E. Shao and Ke-Shan Lin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The change point identification has played a vital role in process improvement for an attribute process. This identification is able to effectively help process personnel to quickly determine the corresponding root causes and significantly improve the underlying process. Although many studies have focused on identifying the change point of a process, a generic identification approach has not been developed. The typical maximum likelihood estimator (MLE) approach has limitations: particularly, the known prior process distribution and mathematical difficulties. These deficiencies are commonly encountered in practice. Accordingly, this study proposes an artificial neural network (ANN) mechanism to overcome the difficulties of typical MLE approach in determining the change point of an attribute process. Specifically, the performance among the statistical process control (SPC) chart alone, the typical MLE approach, and the proposed ANN mechanism are investigated for the following cases: (1) a known attribute process distribution with the associated MLE being available to be used, (2) an unknown attribute process distribution with the MLE being unable to be used, and (3) an unknown attribute process distribution with the MLE being misused. The superior results and the performance of the proposed approach are reported and discussed.