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

نتاج البحث

1 اقتباس (Scopus)

ملخص

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.

اللغة الأصليةEnglish
عنوان منشور المضيف5th Iranian Conference on Signal Processing and Intelligent Systems, ICSPIS 2019
ناشرInstitute of Electrical and Electronics Engineers Inc.
رقم المعيار الدولي للكتب (الإلكتروني)9781728153506
المعرِّفات الرقمية للأشياء
حالة النشرPublished - ديسمبر 2019
منشور خارجيًانعم
الحدث5th Iranian Conference on Signal Processing and Intelligent Systems, ICSPIS 2019 - Shahrood
المدة: ديسمبر ١٨ ٢٠١٩ديسمبر ١٩ ٢٠١٩

سلسلة المنشورات

الاسم5th Iranian Conference on Signal Processing and Intelligent Systems, ICSPIS 2019

Conference

Conference5th Iranian Conference on Signal Processing and Intelligent Systems, ICSPIS 2019
الدولة/الإقليمIran, Islamic Republic of
المدينةShahrood
المدة١٢/١٨/١٩١٢/١٩/١٩

ASJC Scopus subject areas

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