Gradient-Based Recursive Parameter Estimation Methods for a Class of Time-Varying Systems from Noisy Observations DOI
Ning Xu,

Qinyao Liu,

Feng Ding

et al.

Circuits Systems and Signal Processing, Journal Year: 2024, Volume and Issue: 43(11), P. 7089 - 7116

Published: July 29, 2024

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

70
Filtered generalized iterative parameter identification for equation‐error autoregressive models based on the filtering identification idea DOI
Feng Ding, Xingling Shao, Ling Xu

et al.

International Journal of Adaptive Control and Signal Processing, Journal Year: 2024, Volume and Issue: 38(4), P. 1363 - 1385

Published: Jan. 28, 2024

Summary By using the collected batch data and iterative search, based on filtering identification idea, this article investigates proposes a filtered multi‐innovation generalized projection‐based method, gradient‐based least squares‐based method for equation‐error autoregressive systems described by models. These methods can be extended to other linear nonlinear scalar multivariable stochastic with colored noises.

Language: Английский

Citations

61
Hierarchical Gradient-Based Iterative Parameter Estimation Algorithms for a Nonlinear Feedback System Based on the Hierarchical Identification Principle DOI
Dan Yang, Yanjun Liu, Feng Ding

et al.

Circuits Systems and Signal Processing, Journal Year: 2023, Volume and Issue: 43(1), P. 124 - 151

Published: Aug. 17, 2023

Language: Английский

Citations

53
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

49
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

48
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

48
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

45
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

29
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

23
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