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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Statistical process control using optimized neural networks: a case study.

Jalil Addeh1, Ata Ebrahimzadeh2, Milad Azarbad2

  • 1Bargh Gostar Baharan Golestan Corporation, P.O. Box 4971684981, Gonbad Kavus, Iran.

ISA Transactions
|November 12, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces an accurate system for recognizing control chart patterns (CCPs). A hybrid heuristic approach using the cuckoo optimization algorithm (COA) significantly improves recognition accuracy for process monitoring.

Keywords:
COAControl chart patternsNeural networksShape featureStatistical feature

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Area of Science:

  • Industrial Engineering
  • Machine Learning
  • Statistical Process Control

Background:

  • Control charts are essential statistical process control (SPC) tools for detecting process changes.
  • Recognizing control chart patterns (CCPs) accurately is crucial for effective process monitoring and intervention.

Purpose of the Study:

  • To design an accurate and efficient system for control chart pattern (CCP) recognition.
  • To investigate and select the best neural network classifier for CCP recognition.
  • To enhance the generalization performance of the classifier using a hybrid heuristic approach.

Main Methods:

  • Developed an efficient recognition system with feature extraction (shape and statistical features) and classifier modules.
  • Investigated several neural networks (MLP, PNN, RBF) to identify the optimal classifier.
  • Implemented a hybrid heuristic system using the cuckoo optimization algorithm (COA) to improve classifier performance.

Main Results:

  • The proposed system achieved high recognition accuracy for control chart patterns.
  • The feature extraction module effectively identified discriminative characteristics of CCPs.
  • The cuckoo optimization algorithm enhanced the generalization capabilities of the chosen neural network classifier.

Conclusions:

  • The developed system provides an accurate method for CCP recognition.
  • The hybrid heuristic approach, integrating COA, offers superior performance in identifying process deviations.
  • This research contributes to more reliable statistical process control through advanced pattern recognition.