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

DOI10.1080/00207729708929367zbMath1076.68563OpenAlexW2051516107MaRDI QIDQ3124691

H. Brian Hwarng

Publication date: 7 August 1997

Published in: International Journal of Systems Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207729708929367

Mathematics Subject Classification ID

Pattern recognition, speech recognition (68T10)

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Real-time recognition of control chart patterns in autocorrelated processes using a learning vector quantization network-based approach ⋮ Simultaneous identification of mean shift and correlation change in AR(1) processes ⋮ On-line identification of control chart patterns using self-organizing approaches

Cites Work

• X-bar andRcontrol chart interpretation using neural computing
• Detecting process non-randomness through a fast and cumulative learning ART-based pattern recognizer
• Multilayer perceptions for detecting cyclic data on control charts