Field Dependent-Shear Stress Prediction of Magnetorheological Fluid Using an Optimum Extreme Learning Machine Model
Penulis/Author
Irfan Bahiuddin, S.T., M.Phil., Ph.D. (1); Saiful Amri Mazlan (2); Mohd Ibrahim Shapiai (3); Nur Azmah Nordin (4); Fitrian Imaduddin (5); Ubaidillah (6); Nur Azmah Nordin (7); Dimas Adiputra (8)
Tanggal/Date
26 2020
Kata Kunci/Keyword
Abstrak/Abstract
Extreme learning machine (ELM) application to model the shear stress of
magnetorheological (MR) fluids has superiority over the existing methods, such as
Herschel-Bulkley. Although the shear stress has been successfully predicted, the
hidden node numbers are too high reaching up to 10,000 that will hinder the
application of the models. Furthermore, the existing works have tried to determine the
hidden node number only by trial and error method. Therefore, this paper aims to
reduce the hidden node number by employing the particle swarm optimization (PSO)
considering the accuracy and the hidden node numbers. The ELM based-shear stress
model was firstly defined by treating the magnetic field and shear rate as the inputs
and shear stress as output. The objective function optimization method was then
formulated to minimize the normalized error and the hidden node numbers. Finally,
the proposed methods were tested at various ELM activation functions and samples.
The results have shown that the platform has successfully reduced the hidden node
numbers from 10,000 to 571 while maintaining the error of less than 1%. In summary,
the proposed objective function for PSO optimization has successfully built the
optimum shear stress model automatically.
