Data-driven modeling and parameter estimation of nonlinear systems

被引:3
|
作者
Kumar, Kaushal [1 ]
机构
[1] Heidelberg Univ, Inst Math, D-69120 Heidelberg, BW, Germany
来源
EUROPEAN PHYSICAL JOURNAL B | 2023年 / 96卷 / 07期
关键词
TRUST-REGION METHOD; COLORED NOISE; IDENTIFICATION; ALGORITHM; EQUATIONS;
D O I
10.1140/epjb/s10051-023-00574-3
中图分类号
O469 [凝聚态物理学];
学科分类号
070205 ;
摘要
Nonlinear systems play a significant role in numerous scientific and engineering disciplines, and comprehending their behavior is crucial for the development of effective control and prediction strategies. This paper introduces a novel data-driven approach for accurately modeling and estimating parameters of nonlinear systems utilizing trust region optimization. The proposed method is applied to three well-known systems: the Van der Pol oscillator, the Damped oscillator, and the Lorenz system, which find broad applications in engineering, physics, and biology. The results demonstrate the efficacy of the approach in accurately identifying the parameters of these nonlinear systems, enabling a reliable characterization of their behavior. Particularly in chaotic systems like the Lorenz system, capturing the dynamics on the attractor proves to be crucial. Overall, this article presents a robust data-driven approach for parameter estimation in nonlinear dynamical systems, holding promising potential for real-world applications.
引用
收藏
页数:13
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