On-line identification of control chart patterns using self-organizing approaches
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Publication:5460563
DOI10.1080/0020754042000268884zbMath1068.90532OpenAlexW1968849283MaRDI QIDQ5460563
Ruey-Shiang Guh, Yeou-Ren Shiue
Publication date: 18 July 2005
Published in: International Journal of Production Research (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/0020754042000268884
Management decision making, including multiple objectives (90B50) Deterministic network models in operations research (90B10)
Related Items (3)
Decision tree based control chart pattern recognition ⋮ 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
Uses Software
Cites Work
- A neural network approach to identifying cyclic behaviour on control charts: a comparative study
- Tree-Structured Classification Via Generalized Discriminant Analysis
- A neural fuzzy control chart for detecting and classifying process mean shifts
- A neural network approach for the analysis of control chart patterns
- Control chart pattern recognition using learning vector quantization networks
- Detecting process non-randomness through a fast and cumulative learning ART-based pattern recognizer
- Multilayer perceptions for detecting cyclic data on control charts
- Recognition of control chart concurrent patterns using a neural network approach
- An integrated neural network approach for simultaneous monitoring of process mean and variance shifts a comparative study
- A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms
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