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International Journal of Systems Science Volume 28, 1997 - Issue 1
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Original Articles

A neural network approach to identifying cyclic behaviour on control charts: a comparative study

H. BRIAN HWARNG Department of Decision Sciences, National University of Singapore, Singapore, 119260 E-mail: e-mail: [email protected].
Pages 99-112 | Received 22 Feb 1996, Accepted 10 Jul 1996, Published online: 03 Apr 2007

Citations (15)

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Read on this site (3)

R.-S. Guh. (2008) Real-time recognition of control chart patterns in autocorrelated processes using a learning vector quantization network-based approach. International Journal of Production Research 46:14, pages 3959-3991.
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H. Brian Hwarng . (2005) Simultaneous identification of mean shift and correlation change in AR(1) processes. International Journal of Production Research 43:9, pages 1761-1783.
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Ruey-Shiang Guh & Yeou-Ren Shiue. (2005) On-line identification of control chart patterns using self-organizing approaches. International Journal of Production Research 43:6, pages 1225-1254.
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Articles from other publishers (12)

Babar Zaman, Muhammad Riaz, Shabbir Ahmad & Saddam Akber Abbasi. (2014) On artificial neural networking-based process monitoring under bootstrapping using runs rules schemes. The International Journal of Advanced Manufacturing Technology 76:1-4, pages 311-327.
Crossref
H. Brian Hwarng. 2014. Wiley StatsRef: Statistics Reference Online. Wiley StatsRef: Statistics Reference Online.
Wafik Hachicha & Ahmed Ghorbel. (2012) A survey of control-chart pattern-recognition literature (1991–2010) based on a new conceptual classification scheme. Computers & Industrial Engineering 63:1, pages 204-222.
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Ussanee Purintrapiban & H.W. Corley. (2012) Neural networks for detecting cyclic behavior in autocorrelated process. Computers & Industrial Engineering 62:4, pages 1093-1108.
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Shih-Yen Lin, Ruey-Shiang Guh & Yeou-Ren Shiue. (2011) Effective recognition of control chart patterns in autocorrelated data using a support vector machine based approach. Computers & Industrial Engineering 61:4, pages 1123-1134.
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Ibrahim Masood & Adnan Hassan. (2009) Synergistic-ANN Recognizers for Monitoring and Diagnosis of Multivariate Process Shift Patterns. Synergistic-ANN Recognizers for Monitoring and Diagnosis of Multivariate Process Shift Patterns.
Adnan Hassan. (2008) Ensemble ANN-based recognizers to improve classification of X-bar control chart patterns. Ensemble ANN-based recognizers to improve classification of X-bar control chart patterns.
Boyu Wang, Feng Wan & Lianjie Shu. (2008) A modified counter-propagation network for process mean shift identification. A modified counter-propagation network for process mean shift identification.
Ruey-Shiang Guh & Yeou-Ren Shiue. (2008) An effective application of decision tree learning for on-line detection of mean shifts in multivariate control charts. Computers & Industrial Engineering 55:2, pages 475-493.
Crossref
H. Brian Hwarng. 2007. Encyclopedia of Statistics in Quality and Reliability. Encyclopedia of Statistics in Quality and Reliability.
F Zorriassatine, R-S Guh, R M Parkin & J Coy. (2016) Integrating novelty detection, neural networks and conventional tools for pattern recognition in multivariate processes. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 218:7, pages 779-793.
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S.L Ho, M Xie & T.N Goh. (2002) A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction. Computers & Industrial Engineering 42:2-4, pages 371-375.
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