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

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.

Modeling a Practical Dual-Fuel Gas Turbine Power Generation System Using Dynamic Neural Network and Deep Learning

“…For more details about the training algorithms, Levenberg-Marquardt (LM) algorithm, Bayesian regularization (BR) algorithm and scaled conjugate gradient (SCG) algorithm, refer to [23]. According to the input variable u(t) in Equation ( 3), the output from the hidden layer at t time is computed as [22]:…”

“…where w ij is the connection weight between the input neuron u(t − j) and the i th hidden neuron; w ij is the connection weight between the i th hidden neuron and the output feedback delayed loop; a i is the bias of the hidden layer neurons; f 1 (.) is the hidden layer transfer function, i.e., activation function [22]. As mentioned before, the sigmoid function has been used in the proposed code as a hidden layer activation function.…”

Forecasting of Photovoltaic Power by Means of Non-Linear Auto-Regressive Exogenous Artificial Neural Network and Time Series Analysis

“…The NARX neural network technique is an important class of nonlinear recurrent dynamic ANN computer program networks comprising linked nodes inspired by a simplification of the human neural system. As a result, every point contains an artificial neuron that takes one or more inputs and accumulates them and passes through a nonlinear active function to generate an output [32][33][34][35]. In the Feedforward Neural Networks (FNNs) model, the information moves in one direction, with nodes structured in layers.…”

Application of a GA-Optimized NNARX controller to nonlinear chemical and biochemical processes

“…Liu et al. [ 20 ] considered NARX neural networks for analysis and identification of noisy nonlinear magnetorheological (MR) damper systems. The accuracy of their results supports the use of this modeling technique for identifying irregular nonlinear models of MR dampers and similar devices.…”

“…In this regard, various methods have been proposed for activation function selection and network weights and biases tuning via optimization by different algorithms. Liu et al [20] considered NARX neural networks for analysis and identification of noisy nonlinear magnetorheological (MR) damper systems. The accuracy of their results supports the use of this modeling technique for identifying irregular nonlinear models of MR dampers and similar devices.…”