Modelling of Engineering Systems with Small Data: A comparative study

Morteza Mohammadzaheri*, Mojtaba Ghodsi, Hamidreza Ziaiefar, Issam Bahadur, Musaab Zarog, Mohammadreza Emadi, Payam Soltani, Amirhosein Amouzadeh

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

    1 Citation (SciVal)

    Abstract

    This chapter equitably compares five different Artificial Intelligence (AI) techniques for data-driven modelling. All these techniques were used to solve two real-world engineering data-driven modelling problems with small number of experimental data samples, one with sparse and one with dense data. The models of both problems are shown to be highly nonlinear. In the problem with available dense data, Multi-Layer Perceptron (MLP) evidently outperforms other AI models and challenges the claims in the literature about superiority of Fully Connected Cascade (FCC). However, the results of the problem with sparse data shows superiority of FCC, closely followed by MLP and neuro-fuzzy network.
    Original languageEnglish
    Title of host publicationPerspectives and Considerations on the Evolution of Smart Systems
    EditorsMaki Habib
    PublisherIGI Global
    Pages120-136
    Number of pages17
    ISBN (Electronic)9781668476864
    ISBN (Print)9781668476840
    DOIs
    Publication statusPublished (VoR) - 1 Jul 2023

    Keywords

    • Modelling
    • Artificial Intelligence
    • Small Data
    • Sparse Data
    • Dense Data
    • Piezoelectric Actuator
    • Electrical Submersible Pump

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