Bahiuddin, Irfan and Imaduddin, Fitrian and Mazlan, Saiful Amri and Ariff, Mohd. H.M. and Mohmad, Khairunnisa Bte and Ubaidillah, Ubaidillah and Choi, Seung-Bok (2021) Accurate and fast estimation for field-dependent nonlinear damping force of meandering valve-based magnetorheological damper using extreme learning machine method. Sensors and Actuators, A: Physical, 318. ISSN 09244247
Full text not available from this repository. (Request a copy)Abstract
The application of artificial neural network (ANN) models in magnetorheological (MR) damper has gained interest in various studies because of the high accuracy in predicting the damping force, especially for control purposes. However, the existing neural network models have apparent drawbacks such as relatively long training time and the possibility to be trapped in local solutions. Therefore, this paper aims to propose a new method to deal with a highly nonlinear behavior of MR damper using an extreme learning machine (ELM) method. The ELM method is applied to a meandering valve-based MR damper for damping force prediction, which has been recently developed. A simulation scheme is selected with damping force as the output, and current, velocity, and displacement as the inputs. The simulations are then carried out based on fatigue dynamic tests data in various frequencies and currents. The training times for more than nineteen thousand data points using the ELM method with 10, 100, 1000 hidden neuron numbers are less than 1.70 s, which is faster than the conventional ANN. Based on 50 times training processes, the ELM and ANN models have comparable average accuracies with R2 values of more than 0.95. ELM also has shown less value R2 standard deviation showing its advantage to reduce the possibility of being trapped in local solution compared to the conventional ANN. © 2020 Elsevier B.V.
Item Type: | Article |
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Additional Information: | Cited by: 28 |
Uncontrolled Keywords: | Knowledge acquisition; Machine learning; Neural networks; Artificial neural network models; Extreme learning machine; Magneto-rheological dampers; Neural network model; Non-linear damping forces; Nonlinear behavior; Standard deviation; Training process; Damping |
Subjects: | T Technology > TJ Mechanical engineering and machinery |
Divisions: | Vocational School |
Depositing User: | Sri JUNANDI |
Date Deposited: | 28 Oct 2024 01:56 |
Last Modified: | 28 Oct 2024 01:56 |
URI: | https://ir.lib.ugm.ac.id/id/eprint/8569 |