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

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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.

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Authors and Affiliations

  1. Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran

    Ali Ghazizadeh

  2. School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

    Mehrdad Sarani

  3. School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Mahdi Hamid & Ahmad Ghasemkhani

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  1. Ali Ghazizadeh
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  2. Mehrdad Sarani
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  3. Mahdi Hamid
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  4. Ahmad Ghasemkhani
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Correspondence to Mehrdad Sarani.

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Ghazizadeh, A., Sarani, M., Hamid, M. et al. Detecting and estimating the time of a single-step change in nonlinear profiles using artificial neural networks. Int J Syst Assur Eng Manag 14, 74–86 (2023). https://doi.org/10.1007/s13198-021-01121-y

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  • DOI: https://doi.org/10.1007/s13198-021-01121-y

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