Least squares support vector machine method for load identification of nonlinear system

In order to eliminate the dependence of load identification problem on the prior knowledge of current mechanical system, least squares support vector machine was applied to identify the inverse model of nonlinear system, and then based on this inverse model operational responses were adopted to dete...

Full description

Saved in:
Bibliographic Details
Published in:Journal of physics. Conference series 2021-02, Vol.1798 (1), p.12036
Main Authors: Zhou, Pan, Xin, Jianghui, Shang, Weiyan
Format: Article
Language:English
Subjects:
Excitation
Least squares
Mathematical models
Mechanical systems
Model testing
Nonlinear systems
Physics
Support vector machines
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In order to eliminate the dependence of load identification problem on the prior knowledge of current mechanical system, least squares support vector machine was applied to identify the inverse model of nonlinear system, and then based on this inverse model operational responses were adopted to determine real time excitation force. A nonlinear system was applied to conduct the simulation and calculate the steady and unsteady input force in this paper to verify the validity of the proposed method. Simulation results reveal that least squares support vector machine is able to identify reliable inverse model of nonlinear system and then reconstruct accurate real time excitation force. According to the present approach, a small quantity of training samples is needed rather than complete knowledge of the mathematical model and parameters of nonlinear system, so this approach can be extended to engineering application.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1798/1/012036