TY - GEN
T1 - Characteristic Modeling a Class of Nonliear Systems with Different Parameter Estimation Methods
AU - Zeng, Yiyang
AU - Wang, Haoshuai
AU - Chen, Lei
AU - Dong, Zhaoqi
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - In this paper, the true values of the parameters of the difference equation equivalent to a class of nonlinear system state space equations are derived. Four commonly used parameter identification algorithms are used to identify the parameters of the difference equation, and the accuracy of the algorithms is compared. The parameters of the equivalent difference equation for a nonlinear system are identified by using the forgetting factor recurrent least squares (FFRLS), recurrent gradient correction (RGC), recurrent stochastic Newton algorithm (RSNA) and back propagation neural network(BP). By comparing the identification results with the real value, it is found that the least square method is the most accurate parameter identification algorithm.
AB - In this paper, the true values of the parameters of the difference equation equivalent to a class of nonlinear system state space equations are derived. Four commonly used parameter identification algorithms are used to identify the parameters of the difference equation, and the accuracy of the algorithms is compared. The parameters of the equivalent difference equation for a nonlinear system are identified by using the forgetting factor recurrent least squares (FFRLS), recurrent gradient correction (RGC), recurrent stochastic Newton algorithm (RSNA) and back propagation neural network(BP). By comparing the identification results with the real value, it is found that the least square method is the most accurate parameter identification algorithm.
KW - Coefficients identification
KW - Nonlinear system
UR - http://www.scopus.com/pages/publications/105000826317
U2 - 10.1007/978-981-96-2212-2_11
DO - 10.1007/978-981-96-2212-2_11
M3 - Conference contribution
AN - SCOPUS:105000826317
SN - 9789819622115
T3 - Lecture Notes in Electrical Engineering
SP - 104
EP - 115
BT - Advances in Guidance, Navigation and Control - Proceedings of 2024 International Conference on Guidance, Navigation and Control Volume 4
A2 - Yan, Liang
A2 - Duan, Haibin
A2 - Deng, Yimin
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Guidance, Navigation and Control, ICGNC 2024
Y2 - 9 August 2024 through 11 August 2024
ER -