Cited by Auxiliary Model-based Continuous Mixed p-norm Algorithm for Output-error Moving Average Systems Using the Multi-innovation Optimization

Maximum likelihood based multi‐innovation stochastic gradient identification algorithms for bilinear stochastic systems with ARMA noise DOI

Shun An, Yan He, Longjin Wang

et al.

International Journal of Adaptive Control and Signal Processing, Journal Year: 2023, Volume and Issue: 37(10), P. 2690 - 2705

Published: July 13, 2023

Summary This paper considers the parameter estimation problem for bilinear stochastic systems with autoregressive moving average (ARMA) noise using gradient method. First, identification model is derived by eliminating state variables. Based on obtained model, a multi‐innovation generalized extended (MI‐GESG) algorithm proposed theory. Furthermore, to enhance accuracy, maximum likelihood based MI‐GESG (ML‐MI‐GESG) developed principle. Finally, an illustrative simulation example provided testify algorithms. The results show effectiveness of algorithms identifying systems.

Language: Английский

Citations

Novel parameter estimation method for the systems with colored noises by using the filtering identification idea DOI

Ling Xu, Feng Ding, Xiao Zhang

et al.

Systems & Control Letters, Journal Year: 2024, Volume and Issue: 186, P. 105774 - 105774

Published: March 14, 2024

Language: Английский

Citations

Unbiased recursive least squares identification methods for a class of nonlinear systems with irregularly missing data DOI

Wenxuan Liu, Meihang Li

International Journal of Adaptive Control and Signal Processing, Journal Year: 2023, Volume and Issue: 37(8), P. 2247 - 2275

Published: June 6, 2023

Summary Missing data often occur in industrial processes. In order to solve this problem, an auxiliary model and a particle filter are adopted estimate the missing outputs, two unbiased parameter estimation methods developed for class of nonlinear systems (e.g., bilinear systems) with irregularly data. Firstly, is constructed unknown output, model‐based multi‐innovation recursive least squares algorithm presented by expanding scalar innovation vector. Secondly, according bias compensation principle, proposed compensate caused colored noise. Thirdly, further improving accuracy, true output estimated filter, filtering‐based developed. Finally, numerical example selected validate effectiveness algorithms. The simulation results indicate that algorithms have good performance identifying

Language: Английский

Citations

Multi‐innovation gradient‐based iterative identification methods for feedback nonlinear systems by using the decomposition technique DOI

Dan Yang, Feng Ding

International Journal of Robust and Nonlinear Control, Journal Year: 2023, Volume and Issue: 33(13), P. 7755 - 7773

Published: June 3, 2023

Summary This paper studies the parameter estimation problems of feedback nonlinear systems. Combining multi‐innovation identification theory with negative gradient search, we derive a gradient‐based iterative algorithm. In order to reduce computational burden and further improve accuracy, decomposition algorithm is proposed by using technique. The key transform an original system into two subsystems estimate parameters each subsystem, respectively. A simulation example provided demonstrate effectiveness algorithms.

Language: Английский

Citations

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

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

Hierarchical recursive least squares parameter estimation methods for multiple‐input multiple‐output systems by using the auxiliary models DOI

Haoming Xing,

Feng Ding, Feng Pan

et al.

International Journal of Adaptive Control and Signal Processing, Journal Year: 2023, Volume and Issue: 37(11), P. 2983 - 3007

Published: Aug. 23, 2023

Summary Multiple‐input multiple‐output (MIMO) models are widely used in practical engineering. This article derives a new identification model of the MIMO system by decomposing into several multiple‐input single‐output subsystems. By means auxiliary idea, an model‐based recursive least squares (AM‐RLS) algorithm is derived for identifying systems. In order to reduce computational burden systems, this presents hierarchical applying principle, (AM‐HLS) proposed improving efficiency. The efficiency analysis indicates that AM‐HLS effective reducing calculation amount compared with AM‐RLS algorithm. Moreover, analyzes convergence simulation example shows and algorithms studied effective.

Language: Английский

Citations

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

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

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