Prediction of magnetorheological grease compositions using extreme learning machine methods

This paper presents a data-driven model to predict magnetorheological (MR) grease composition as a function of its rheological properties using several machine learning methods. The methods are Single Hidden Layer Feedforward Neural Networks (SLFNs) and Kernel Based-Extreme Learning Ma-chine (KELM)....

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Hauptverfasser:	Bahiuddin, Irfan, Pratama, Nico, Imaduddin, Fitrian, Mazlan, Saiful Amri, Ubaidillah, Mohamad, Norzilawati
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Artificial neural networks Back propagation networks Carbonyls Composition Greases Kernel functions Machine learning Magnetic fields Magnetic properties Neural networks Polynomials Radial basis function Rheological properties Yield strength Yield stress
Online-Zugang:	Volltext
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