A Special-Purpose Neural Network Recogniser to Detect Non-Random Pattern on Control Charts

  • A. Anglani
  • M. Pacella
  • Q. Semeraro
Conference paper


With the growing employment of automatic data-collection methods and the enhancements on computerised plotting on control charts a demand exists to automate the analysis of process data. Comterised recognition techniques can provide an actual alternative to conventional methods for analysing control charts with little or no human intervention. In this paper, a neural network approach is discussed and applied to trend-pattern recognition on control charts. In the proposed approach the neural network is trained to recognise both “natural” and “unnatural” distribution of points. Experimental results are compared to a combined Shewhart-CUSUM approach in terms of Average Run Length (ARL).


Neural Network Control Chart Unnatural Pattern Neural Network Architecture Elman Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Wien 2001

Authors and Affiliations

  • A. Anglani
  • M. Pacella
    • 1
  • Q. Semeraro
    • 2
  1. 1.Dip. di Ing. InnovazioneUniversità degli Studi di LecceLecceItaly
  2. 2.Dip. di MeccanicaPolitecnico di MilanoMilanoItaly

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