Adaptive resonance theory-based neural algorithms for manufacturing process quality control

DOI10.1080/00207540410001715706zbMath1060.90606OpenAlexW2042148762MaRDI QIDQ4671344

Massimo Pacella, Quirico Semeraro, Alfredo Anglani

Publication date: 26 April 2005

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

Full work available at URL: https://doi.org/10.1080/00207540410001715706

Mathematics Subject Classification ID

Production models (90B30)

Related Items (2)

Decision tree based control chart pattern recognition ⋮ Exploring the non-linearity in empirical modelling of a steel system using statistical and neural network models

Uses Software

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Cites Work

• A comparative study of some pseudorandom number generators
• Exact Results for Shewhart Control Charts with Supplementary Runs Rules
• A neural network approach for the analysis of control chart patterns
• Detecting process non-randomness through a fast and cumulative learning ART-based pattern recognizer
• Recognition of control chart concurrent patterns using a neural network approach
• Detecting process mean shift in the presence of autocorrelation: a neural-network based monitoring scheme

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