Frequency-domain recursive hybrid GA to parameter identification of structural systems with added-damping-and-stiffness devices

2020 ◽  
Author(s):  
Grace Wang ◽  
Wai-Choun Liew ◽  
Fu-Kuo Huang
Keyword(s):  
Parameter Identification ◽  
Frequency Domain ◽  
Structural Systems

Fourier Series-Based Neural Networks for Vehicle System Identification

1999 ◽  
Author(s):  
Imtiaz Haque ◽  
Juergen Schuller
Keyword(s):  
Neural Networks ◽  
Fourier Series ◽  
System Identification ◽  
Transfer Function ◽  
Parameter Identification ◽  
Frequency Domain ◽  
Vehicle System ◽  
Non Linear ◽  
Function Identification ◽  
Non Linear System

Abstract The use of neural networks in system identification is an emerging field. Neural networks have become popular in recent years as a means to identify linear and non-linear systems whose characteristics are unknown. The success of sigmoidal networks in parameter identification has been limited. However, harmonic activation-based neural networks, recent arrivals in the field of neural networks, have shown excellent promise in linear and non-linear system parameter identification. They have been shown to have excellent generalization capability, computational parallelism, absence of local minima, and good convergence properties. They can be used in the time and frequency domain. This paper presents the application of a special class of such networks, namely Fourier Series neural networks (FSNN) to vehicle system identification. In this paper, the applications of the FSNNs are limited to the frequency domain. Two examples are presented. The results of the identification are based on simulation data. The first example demonstrates the transfer function identification of a two-degree-of freedom lateral dynamics model of an automobile. The second example involves transfer function identification for a quarter car model. The network set-up for such identification is described. The results of the network identification are compared with theory. The results indicate excellent prediction properties of such networks.

Structural Parameter Identification in the Frequency Domain: The Use of Overdetermined Systems

1987 ◽  
Vol 109 (2) ◽  
pp. 120-123 ◽  
Author(s):  
S. G. Braun ◽  
Y. M. Ram
Keyword(s):  
Transfer Function ◽  
Parameter Identification ◽  
Frequency Domain ◽  
Rational Function ◽  
Least Square ◽  
Structural Parameter ◽  
Overdetermined Systems ◽  
The Matrix ◽  
Domain Transfer

This paper deals with the fitting of a rational function to an experimentally determined frequency domain transfer function. Examples show that the use of overdetermined fitted systems improve the identification in noisy situations. A method is presented, enabling the determination of the number of zero/poles of the system, and the relation to the rank of the matrix used in a least square identification method.

scholarly journals Parameter Identification of Weakly Nonlinear Vibration System in Frequency Domain

2004 ◽  
Vol 11 (5-6) ◽  
pp. 685-692 ◽  
Author(s):  
Jiehua Peng ◽  
Jiashi Tang ◽  
Zili Chen
Keyword(s):  
Parameter Identification ◽  
Frequency Response ◽  
Nonlinear Vibration ◽  
Frequency Domain ◽  
Response Function ◽  
Frequency Response Function ◽  
Physical Parameters ◽  
Vibration System ◽  
Weakly Nonlinear ◽  
Nonlinear Vibration System

A new method of identifying parameters of nonlinearly vibrating system in frequency domain is presented in this paper. The problems of parameter identification of the nonlinear dynamic system with nonlinear elastic force or nonlinear damping force are discussed. In the method, the mathematic model of parameter identification is frequency response function. Firstly, by means of perturbation method the frequency response function of weakly nonlinear vibration system is derived. Next, a parameter transformation is made and the frequency response function becomes a linear function of the new parameters. Then, based on this function and with the least square method, physical parameters of the system are identified. Finally, the applicability of the proposed technique is confirmed by numerical simulation.

Parameter identification and dynamic response analysis of a modified Prandtl–Ishlinskii asymmetric hysteresis model via least-mean square algorithm and particle swarm optimization

2021 ◽  
pp. 146442072110068
Author(s):  
Tianyu Wang ◽  
Mohammad Noori ◽  
Wael A Altabey ◽  
Mojtaba Farrokh ◽  
Ramin Ghiasi
Keyword(s):  
Particle Swarm Optimization ◽  
Dynamic Response ◽  
Parameter Identification ◽  
Response Analysis ◽  
Structural Systems ◽  
Mean Square ◽  
Least Mean Square ◽  
Hysteresis Model ◽  
Swarm Optimization ◽  
Least Mean Square Algorithm

Hysteresis is a nonlinear phenomenon observed in the dynamic response behavior of numerous structural systems under high intensity cyclic or random loading, as well as in numerous mechanical and electromagnetic systems. For several decades, hysteretic response analysis of structural systems has been widely studied and numerous hysteresis models have been proposed and utilized in order to reproduce and better understand the complex hysteretically degrading behavior of structural systems. An important area of research in this regard has been the parameter identification of hysteretic systems. In this paper, we propose a modified Prandtl–Ishlinskii model to simulate the asymmetric hysteresis, which is the complex behavior in structural systems. In addition, a new approach based on particle swarm optimization and least-mean square algorithm is utilized for parameter identification of this hysteresis model. Finally, the model is applied in structural dynamic response analysis of a base isolated structural model under seismic load.

Simulation of Fluid Sloshing for Decreasing the Response of Structural Systems

2014 ◽  
Author(s):  
İlker Vuruşkan ◽  
Cüneyt Sert ◽  
Mehmet Bülent Özer
Keyword(s):  
Frequency Domain ◽  
Structural Model ◽  
Harmonic Component ◽  
Structural Response ◽  
Coupled System ◽  
Frequency Domain Analysis ◽  
Surface Shape ◽  
Structural Systems ◽  
Total Force ◽  
Ansys Fluent

In the last decade, there is a renewed interest in the integration of a sloshing tank into structural systems to decrease the vibrations of the structure. The purpose of this study is to try different numerical simulation programs for further use in studies in evaluation of the effectiveness of the sloshing tank absorbers for structural systems. The programs chosen for sloshing simulations are COMSOL Multiphysics®, ANSYS CFX and ANSYS-FLUENT. In the numerical simulations, the free surface shape during sloshing will be simulated under small and large amplitude sinusoidal displacements. The results obtained using different software will be compared with the results of the experiments reported in literature. Since the purpose is to use the sloshing forces on the container to decrease the structural response, the total force on the container walls is calculated and compared with the reported experimental results. The dynamics of a container coupled with the a structural model is simulated and forces applied on the container walls are analyzed in the frequency domain which is important in understanding the tuning of the vibration absorber. To the best of authors’ knowledge, in a fluid-structure coupled system the frequency domain analysis of the container wall forces at varying amplitudes of sinusoidal excitation is not presented in literature. The results showed even though higher harmonic forcing magnitudes increase with increasing base motion, the fundamental harmonic component does not change significantly.

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