The Box-Cox Transformation-Based ARFNNs for Identification of Nonlinear MR Damper System with Outliers and Skewness Noises

Chen, Pi Yun; Fu, Yu Yi; Su, Kuo-Lan; Jeng, Jin Tsong

doi:10.4028/www.scientific.net/amm.284-287.2120

Cited by 1 publication

(1 citation statement)

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“…As this paper attempts to propose an inverse model to predict an MR fluid composition for the first time, FFNNs can be a suitable candidate. FFNN has been applied in various fields and can be trained using various optimization algorithms, such [33][34][35]. SVM and ANFIS can be a great alternative to neural networks that can be discussed further in the future.…”

Section: Ffnn Modelmentioning

confidence: 99%

A machine learning approach to estimate magnetorheological suspension composition based on magnetic field dependent-rheological properties

Bahiuddin¹,

Imaduddin²,

Mazlan³

et al. 2021

Smart Mater. Struct.

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This paper presents an inverse model of magnetorheological (MR) suspensions to predict the compositions as a function of magnetic field-dependent rheological properties using the feedforward neural network (FFNN) model. Although many variations of MR suspension compositions have been published, the composition of the MR suspensions needs to be chosen manually by considering the required rheological properties. Therefore, this paper proposed a systematic method to predict MR suspension composition based on the rheological properties by employing extreme learning machine (ELM) and backpropagation (BP) techniques to build FFNN models. The model is built based on three experimental datasets representing particle shapes, sizes, and weight percentages. Three input topologies are proposed involving yield stress at the on-state condition, yield stress at the off-state condition τ y,0 , yield stress slope m, and magnetic fields B. The outputs are the particle weight percentages, milling time of magnetic particles representing the particle shapes, and particle sizes for each dataset model. FFNN models trained by ELM and BP have shown comparable accuracy. The simulations have also shown that the inclusion of the slope as one of the model inputs can produce R 2 of more than 0.80 for training and testing data. The off-state condition of yield stress can also improve the model performances if the off-state yield stress has a high correlation with the output. Finally, from the results, it can be concluded that the proposed machine learning approaches and topologies have successfully predicted the compositions in good agreement with the experimental data.

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Section: Ffnn Modelmentioning

confidence: 99%