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A novel machine learning algorithm for interval systems approximation based on artificial neural network

Author

Listed:
  • Raouf Zerrougui

    (Universite de Djelfa-ALGERIE)

  • Amel B. H. Adamou-Mitiche

    (Universite de Djelfa-ALGERIE)

  • Lahcene Mitiche

    (Universite de Djelfa-ALGERIE)

Abstract

In recent years, order-reduction techniques based on artificial intelligence algorithms have become a topic of interest in the structural dynamics community. In this paper, we describe a new artificial intelligence technique based on the artificial neural network used to reduce a large interval system. Applied to reduce the degree of the polynomial numerator and denominator each separately, by allowing them to learn automatically from the original system, this machine learning phase allows the algorithm to improve over time and control performance of the approximation, maintaining as much as possible the stability of the dynamic system, and reducing errors between the original system and the reduced system that are presented as a very acceptable approximation, a comparison study is presented between existing works and the proposed technique, with the help of examples from literature.

Suggested Citation

  • Raouf Zerrougui & Amel B. H. Adamou-Mitiche & Lahcene Mitiche, 2023. "A novel machine learning algorithm for interval systems approximation based on artificial neural network," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2171-2184, June.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:5:d:10.1007_s10845-021-01874-0
    DOI: 10.1007/s10845-021-01874-0
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    References listed on IDEAS

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    1. Ashish Kumar & Roussos Dimitrakopoulos & Marco Maulen, 2020. "Adaptive self-learning mechanisms for updating short-term production decisions in an industrial mining complex," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1795-1811, October.
    2. Zhiwen Huang & Jianmin Zhu & Jingtao Lei & Xiaoru Li & Fengqing Tian, 2020. "Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 953-966, April.
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