Nonlinear System Identification Based on Combined Signal Sources

Authors

Tian Zheng, 1.College of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China;Follow
Feng Li, 1.College of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China;Follow
Naibao He, 1.College of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China;
Ya Gu, 2.College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 201418, China;

Abstract

Abstract: Aiming at the interference of noise in the nonlinear system, the identification modeling method of the neuro-fuzzy Hammerstein output error nonlinear system is considered. The combined signal sources are used to realize the parameter identification separation of the linear block and the nonlinear block. The correlation analysis method and the recursive least square identification method based on auxiliary model technique are derived to estimate the parameters of dynamic linear block and nonlinear block, which can effectively suppress the interference of system output noise. Compared with least square algorithm, polynomial model and multi-innovation method, the simulation results demonstrate that the proposed approach has the advantages of fast convergence speed of parameter estimation, high identification accuracy and small modeling error, which verifies the effectiveness of the proposed approach.

Recommended Citation

Zheng, Tian; Li, Feng; He, Naibao; and Gu, Ya (2022) "Nonlinear System Identification Based on Combined Signal Sources," Journal of System Simulation: Vol. 34: Iss. 11, Article 7.
DOI: 10.16182/j.issn1004731x.joss.21-0589
Available at: https://dc-china-simulation.researchcommons.org/journal/vol34/iss11/7

First Page

2377

Revised Date

2021-08-20

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.21-0589

Last Page

2385

CLC

TP273;TP391.9

Recommended Citation

Tian Zheng, Feng Li, Naibao He, Ya Gu. Nonlinear System Identification Based on Combined Signal Sources[J]. Journal of System Simulation, 2022, 34(11): 2377-2385.

Corresponding Author

Feng Li,lifeng@jsut.edu.cn

DOI

10.16182/j.issn1004731x.joss.21-0589