Izvorni znanstveni članak
https://doi.org/10.1080/00051144.2022.2087843
Identification of a nonlinear rational model based on bias compensated multi-innovation stochastic gradient algorithm
S. X. Jing
; School of Physics and Electronic Electrical Engineering, Huaiyin Normal University, Huaian, People’s Republic of China
R. R. Liu
; School of Automotive and Traffic Engineering, Jiangsu University of Technology, Changzhou, People’s Republic of China
L. Chen
; School of Physics and Electronic Electrical Engineering, Huaiyin Normal University, Huaian, People’s Republic of China
Sažetak
The nonlinear rational model is a generalized nonlinear model and has been gradually applied in modelling many dynamic processes. The parameter identification of a class of nonlinear rational models is studied in this paper. This identification problem is very challenging because of the complexity of the rational model and the coupling between model inputs and outputs. To identify the nonlinear model, a bias compensated multi-innovation stochastic gradient algorithm is presented. The multi-innovation technique replacing the scalar innovation with an information vector is adopted to accelerate the traditional stochastic gradient algorithm. However, the estimate obtained by the accelerated algorithm is biased because of the correlation between the information vector and the noise. To overcome this difficulty, a bias compensation strategy is used. The bias is calculated and compensated to get an unbiased estimate. Theoretical analysis shows that the proposed algorithm can give biased estimates with linear complexity. The proposed algorithm is validated by a numerical experiment and the modelling of the propylene catalytic oxidation.
Ključne riječi
Nonlinear rational model; parameter estimation; adaptive modelling; bias compensation; stochastic gradient algorithm
Hrčak ID:
287875
URI
Datum izdavanja:
13.6.2022.
Posjeta: 203 *