Modelling and Analysis of Complex System Dynamics Based on Orthogonal Matching Pursuit Algorithm

doi:10.3901/JME.2022.19.086

Abstract

Abstract: The problem of identifying non-linear system models is addressed by introducing the OMP (Orthogonal matching pursuit) algorithm for fast non-linear system modelling. The method aims to solve the problem of poor timeliness in modelling large data with NARX (Nonlinear autoregressive with exogenous inputs) models. Firstly, it is shown that the OLS (Orthogonal least squares) algorithm has the problem of many orthogonal times and time consuming, which can be effectively solved by using the OMP algorithm. The kinetic properties of the NARX model obtained by the OMP algorithm are verified using the model prediction method. Secondly, the effectiveness of the OMP algorithm system modelling is illustrated by taking a single degree of freedom non-linear system as an example. Finally, the NARX model of the cantilever beam is established using the OMP algorithm, and the NARX model prediction output is compared with the experimental measured output, the inherent frequency of the NARX model and the actual inherent frequency of the cantilever beam respectively. The results show that the modelling efficiency of the proposed method is on average 10 times higher than that of the OLS algorithm, and the model can effectively reflect the dynamics of the system.

Key words: nonlinear systems, NARX model, OLS algorithm, OMP algorithm, variance analysis

CLC Number:

TG156

LUO Zhong, ZHOU Guangze, ZHU Yunpeng, GAO Yi, LI Lei. Modelling and Analysis of Complex System Dynamics Based on Orthogonal Matching Pursuit Algorithm[J]. Journal of Mechanical Engineering, 2022, 58(19): 86-94.

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