基于高阶关联矩阵方法的混沌系统参数辨识

doi:10.13190/j.jbupt.2019-021

北京邮电大学学报 ›› 2019, Vol. 42 ›› Issue (5): 15-21.doi: 10.13190/j.jbupt.2019-021

基于高阶关联矩阵方法的混沌系统参数辨识

吴林冲¹, 颜子翔², 肖井华¹

1. 北京邮电大学理学院, 北京 100876;
2. 北京大学物理学院, 北京 100871

收稿日期:2019-02-07 出版日期:2019-10-28 发布日期:2019-11-25
通讯作者: 肖井华(1965-),男,教授,博士生导师,E-mail:jhxiao@bupt.edu.cn. E-mail:jhxiao@bupt.edu.cn
作者简介:吴林冲(1994-),男,硕士生.
基金资助:
国家自然科学基金项目（11775034）

Parameter Identification of Chaotic Systems Based on High-Order Correlation Computations

WU Lin-chong¹, YAN Zi-xiang², XIAO Jing-hua¹

1. School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China;
2. School of Physics, Peking University, Beijing 100871, China

Received:2019-02-07 Online:2019-10-28 Published:2019-11-25

摘要/Abstract

摘要： 为了减小系统非线性和噪声对参数辨识效果的影响，采用缺失变量的高阶关联矩阵方法（HOCC），以处于混沌状态的蔡氏电路为研究对象，分别对模拟蔡氏电路以及存在噪声的单个实验蔡氏电路和耦合实验蔡氏电路进行动力学参数辨识.研究结果表明，该方法在蔡氏电路电感电流未知情况下进行参数辨识是有效的，同时对噪声具有很好的鲁棒性.所得的参数辨识结果和电路标定参数的相对差距均能在20%的范围之内.该研究结果可以为蔡氏电路中器件参数测量提供参考依据，并能进一步应用到其他网络结构参数辨识实验中.

关键词: 缺失变量, 高阶关联矩阵方法, 蔡氏电路, 参数辨识, 鲁棒性

Abstract: In order to reduce the influence of system nonlinearity and noise on the effect of parameter identification, the high-order correlation computations (HOCC) with missing variables and noise is used to identify the dynamic parameters of real chaotic Chua's circuit, based on both simulation and experimental data of a single chua's circuit and a coupled chua's circuit. The results showed that the relative errors between identifications and calibration parameters of the experiments can be controlled within 20%, which means HOCC not only has good robustness to noise but also solves the problem produced by the missing data of the current of inductance. This work provided a benchmark for the measurement of instrument parameters of Chua's circuit, and could further apply on the parameter identification of other complicated networks.

Key words: missing variables, high-order correlation computations, Chua's circuit, parameter identification, robustness

中图分类号:

O415.5

吴林冲, 颜子翔, 肖井华. 基于高阶关联矩阵方法的混沌系统参数辨识[J]. 北京邮电大学学报, 2019, 42(5): 15-21.

WU Lin-chong, YAN Zi-xiang, XIAO Jing-hua. Parameter Identification of Chaotic Systems Based on High-Order Correlation Computations[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2019, 42(5): 15-21.

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基于高阶关联矩阵方法的混沌系统参数辨识

Parameter Identification of Chaotic Systems Based on High-Order Correlation Computations

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