Data-driven Modelling of Engineering Systems with Small Data, a Comparative Study of Artificial Intelligence Techniques

Morteza Mohammadzaheri, Hamidreza Ziaiefar, Issam Bahadur, Musaab Zarog, Mohammadreza Emadi, Mojtaba Ghodsi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper equitably compares five different Artificial Intelligence (AI) models and a linear model to tackle two real-world engineering data-driven modelling problems with small number of experimental data. Analysis of results show that, in both cases, the models are highly nonlinear and Multi-Layer Perceptrons (MLPs) outperform other AI models including neuro-fuzzy networks (or in short fuzzy models), Radial Basis Function Networks (RBFNs) and Fully Connected Cascade (FCC) networks. The latter has been claimed to be superior in the literature for some non-engineering benchmarks.

Original languageEnglish
Title of host publication5th Iranian Conference on Signal Processing and Intelligent Systems, ICSPIS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728153506
DOIs
Publication statusPublished - Dec 1 2019
Externally publishedYes
Event5th Iranian Conference on Signal Processing and Intelligent Systems, ICSPIS 2019 - Shahrood, Iran, Islamic Republic of
Duration: Dec 18 2019Dec 19 2019

Publication series

Name5th Iranian Conference on Signal Processing and Intelligent Systems, ICSPIS 2019

Conference

Conference5th Iranian Conference on Signal Processing and Intelligent Systems, ICSPIS 2019
CountryIran, Islamic Republic of
CityShahrood
Period12/18/1912/19/19

ASJC Scopus subject areas

  • Artificial Intelligence
  • Signal Processing
  • Information Systems and Management
  • Health Informatics

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