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Detecting and estimating the time of a single-step change in nonlinear profiles using artificial neural networks

Author

Listed:
  • Ali Ghazizadeh

    (Sharif University of Technology)

  • Mehrdad Sarani

    (Iran University of Science and Technology)

  • Mahdi Hamid

    (University of Tehran)

  • Ahmad Ghasemkhani

    (University of Tehran)

Abstract

This effort attempts to study the change point problem in the area of non-linear profiles. A method based on Artificial Neural Networks (ANN) is proposed for estimating the real time of a single step change. The feature vector of the proposed Multi-Layer Perceptron (MLP) is based on Z and control chart statistics for nonlinear profiles. The merits of the proposed estimator are evaluated through simulation experiments. The results show that the estimator provides an accurate estimate of the single step change point in non-linear profiles in the selected case problem.

Suggested Citation

  • Ali Ghazizadeh & Mehrdad Sarani & Mahdi Hamid & Ahmad Ghasemkhani, 2023. "Detecting and estimating the time of a single-step change in nonlinear profiles using artificial neural networks," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 74-86, February.
    • Handle: RePEc:spr:ijsaem:v:14:y:2023:i:1:d:10.1007_s13198-021-01121-y
    DOI: 10.1007/s13198-021-01121-y
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    References listed on IDEAS

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    1. Alireza Sharafi & Majid Aminnayeri & Amirhossein Amiri, 2014. "Estimating the change point of binary profiles in phase II," International Journal of Productivity and Quality Management, Inderscience Enterprises Ltd, vol. 14(3), pages 336-351.
    2. H. B. Hwarng, 2008. "Toward identifying the source of mean shifts in multivariate SPC: a neural network approach," International Journal of Production Research, Taylor & Francis Journals, vol. 46(20), pages 5531-5559, January.
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