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An Optimal NARX Neural Network Identification Model for a Magnetorheological Damper With Force-Distortion Behavior

Liu, Qianjie and Chen, Wei and Hu, Huosheng and Zhu, Qingyuan and Xie, Zhixiang (2020) 'An Optimal NARX Neural Network Identification Model for a Magnetorheological Damper With Force-Distortion Behavior.' Frontiers in Materials, 7. ISSN 2296-8016

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Abstract

This paper presents an optimal NARX neural network identification model for a magnetorheological (MR) damper with the force-distortion behavior. An intensive experimental study is conducted for designing the NARX network architecture to enhance modeling accuracy and availability, and the activation function selection, weights, and biases of the selected network are optimized by differential evolution algorithm. Different experimental training and validation samples are used for network training. The prediction capability of the optimal NARX model is verified by new measured test data. The test and comparative results show that the optimal NARX network model can satisfactorily emulate the dynamic behavior of MR damper and effectively capture its distortion behavior occurred with the increased current. The developed inverse NARX network model can effectively estimate the required current and track desired damping force. Moreover, the effects of different noise disturbance on the NARX network model performance are analyzed, and the model error varies slightly with a small noise disturbance. The accuracy of the results supports the use of this modeling technique for identifying irregular non-linear models of MR damper and similar devices.

Item Type: Article
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 10 Jun 2020 13:56
Last Modified: 10 Jun 2020 13:56
URI: http://repository.essex.ac.uk/id/eprint/27632

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