A coupled recursive least squares algorithm for multivariable systems and its computational amount analysis by using the coupling identification concept DOI Open Access
Yu Jin, Feng Ding

International Journal of Adaptive Control and Signal Processing, Journal Year: 2023, Volume and Issue: 38(2), P. 513 - 533

Published: Nov. 15, 2023

Summary In order to solve the problem of parameter identification for large‐scale multivariable systems, which leads a large amount computation algorithms, two recursive least squares algorithms are derived according characteristics systems. To further reduce and cut down redundant estimation, we propose coupled algorithm based on coupling concept. By same estimates between sub‐identification estimation subsystem vectors avoided. Compared with proposed in this article have higher computational efficiency smaller errors. Finally, simulation example tests effectiveness algorithm.

Language: Английский

Recursive identification methods for general stochastic systems with colored noises by using the hierarchical identification principle and the filtering identification idea DOI
Feng Ding, Ling Xu, Xiao Zhang

et al.

Annual Reviews in Control, Journal Year: 2024, Volume and Issue: 57, P. 100942 - 100942

Published: Jan. 1, 2024

Language: Английский

Citations

74

Adaptive Multi-Innovation Gradient Identification Algorithms for a Controlled Autoregressive Autoregressive Moving Average Model DOI
Ling Xu, Huan Xu, Feng Ding

et al.

Circuits Systems and Signal Processing, Journal Year: 2024, Volume and Issue: 43(6), P. 3718 - 3747

Published: March 13, 2024

Language: Английский

Citations

56

Joint iterative state and parameter estimation for bilinear systems with autoregressive noises via the data filtering DOI
Siyu Liu, Yanjiao Wang, Feng Ding

et al.

ISA Transactions, Journal Year: 2024, Volume and Issue: 147, P. 337 - 349

Published: Feb. 3, 2024

Language: Английский

Citations

53

Highly-efficient filtered hierarchical identification algorithms for multiple-input multiple-output systems with colored noises DOI

Haoming Xing,

Feng Ding, Xiao Zhang

et al.

Systems & Control Letters, Journal Year: 2024, Volume and Issue: 186, P. 105762 - 105762

Published: March 14, 2024

Language: Английский

Citations

51

A novel recursive multivariate nonlinear time-series modeling method by using the coupling identification concept DOI
Yihong Zhou, Feng Ding

Applied Mathematical Modelling, Journal Year: 2023, Volume and Issue: 127, P. 571 - 587

Published: Dec. 7, 2023

Language: Английский

Citations

50

Online identification methods for a class of Hammerstein nonlinear systems using the adaptive particle filtering DOI
Huan Xu, Ling Xu, Shaobo Shen

et al.

Chaos Solitons & Fractals, Journal Year: 2024, Volume and Issue: 186, P. 115181 - 115181

Published: July 1, 2024

Language: Английский

Citations

35

Online identification of Hammerstein systems with B‐spline networks DOI
Yanjiao Wang, Yiting Liu, Jiehao Chen

et al.

International Journal of Adaptive Control and Signal Processing, Journal Year: 2024, Volume and Issue: 38(6), P. 2074 - 2092

Published: March 27, 2024

Summary Nonlinear systems widely exist in real‐word applications and the research for these has enjoyed a long fruitful history, including system identification community. However, modeling nonlinear is often quite challenging still remains many unresolved questions. This article considers online issue of Hammerstein systems, whose static function modeled by B‐spline network. First, model studied constructed using bilinear parameter decomposition model. Second, recursive algorithms are proposed to find estimates moving data window particle swarm optimization procedure, show that converge their true values with low computational burden. Numerical examples also given test effectiveness algorithms.

Language: Английский

Citations

25

The filtering-based recursive least squares identification and convergence analysis for nonlinear feedback control systems with coloured noises DOI
Ling Xu, Huan Xu, Chun Wei

et al.

International Journal of Systems Science, Journal Year: 2024, Volume and Issue: 55(16), P. 3461 - 3484

Published: July 7, 2024

Language: Английский

Citations

22

Decomposition‐based maximum likelihood gradient iterative algorithm for multivariate systems with colored noise DOI
Lijuan Liu

International Journal of Robust and Nonlinear Control, Journal Year: 2024, Volume and Issue: 34(11), P. 7265 - 7284

Published: March 28, 2024

Summary In this paper, we use the maximum likelihood principle and negative gradient search to study identification issues of multivariate equation‐error systems whose outputs are contaminated by an moving average noise process. The model decomposition technique is used decompose system into several regressive subsystems based on number outputs. order improve parameter estimation accuracy, a decomposition‐based iterative algorithm proposed means method. numerical simulation example indicates that method has better results than compared algorithm.

Language: Английский

Citations

20

Data Filtering-Based Maximum Likelihood Gradient-Based Iterative Algorithm for Input Nonlinear Box–Jenkins Systems with Saturation Nonlinearity DOI
Yamin Fan, Ximei Liu, Meihang Li

et al.

Circuits Systems and Signal Processing, Journal Year: 2024, Volume and Issue: 43(11), P. 6874 - 6910

Published: Aug. 1, 2024

Language: Английский

Citations

20