Modelling of Engineering Systems with Small Data, a Comparative Study

Mohammadzaheri, Morteza and Ziaiefar, Hanmidreza and Ghodsi, Mojtaba and Bahadur, Issam and Zarog, Musaab and Emadi, Mohammadreza and Amouzadeh, Amirhosein (2023) Modelling of Engineering Systems with Small Data, a Comparative Study. In: Perspectives and Considerations on the Evolution of Smart Systems. IGI Global. ISBN 9781668476840

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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.

Item Type:	Book Section
Dates:	Date Event 3 June 2023 Accepted 31 July 2023 Published
Uncontrolled Keywords:	Modelling, Artificial Intelligence, Small Data, Sparse Data, Dense Data, Piezoelectric Actuator, Electrical Submersible Pump
Subjects:	CAH00 - multidisciplinary > CAH00-00 - multidisciplinary > CAH00-00-00 - multidisciplinary CAH10 - engineering and technology > CAH10-01 - engineering > CAH10-01-01 - engineering (non-specific) CAH10 - engineering and technology > CAH10-01 - engineering > CAH10-01-02 - mechanical engineering CAH10 - engineering and technology > CAH10-01 - engineering > CAH10-01-08 - electrical and electronic engineering CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions:	Faculty of Computing, Engineering and the Built Environment > College of Engineering
Depositing User:	Morteza Mohammadzaheri
Date Deposited:	26 Jul 2023 13:57
Last Modified:	20 Jun 2024 11:50
URI:	https://www.open-access.bcu.ac.uk/id/eprint/14624

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