Detecting process non-randomness through a fast and cumulative learning ART-based pattern recognizer

DOI10.1080/00207549508904783zbMath0912.90153OpenAlexW1972271278MaRDI QIDQ4399012

Chee-Way Chong, H. Brian Hwarng

Publication date: 2 May 1999

Published in: International Journal of Production Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207549508904783

zbMATH Keywords

neural networksadaptive resonance theorycontrol chart pattern recognizer

Mathematics Subject Classification ID

Production models (90B30) Pattern recognition, speech recognition (68T10)

Related Items (11)

Shift detection and source identification in multivariate autocorrelated processes ⋮ Real-time recognition of control chart patterns in autocorrelated processes using a learning vector quantization network-based approach ⋮ Decision tree based control chart pattern recognition ⋮ Toward identifying the source of mean shifts in multivariate SPC: a neural network approach ⋮ Adaptive resonance theory-based neural algorithms for manufacturing process quality control ⋮ Simultaneous identification of mean shift and correlation change in AR(1) processes ⋮ A hybrid learning-based model for on-line monitoring and diagnosis of out-of-control signals in multivariate manufacturing processes ⋮ Using anMQEchart based on a self-organizing map NN to monitor out-of-control signals in manufacturing processes ⋮ On-line identification of control chart patterns using self-organizing approaches ⋮ A bibliography of neural network business applications research: 1994--1998 ⋮ A neural network approach to identifying cyclic behaviour on control charts: a comparative study

Cites Work

• A massively parallel architecture for a self-organizing neural pattern recognition machine

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