Correntropy‐Based Robust Interval‐Varying Recursive Estimation Method for Nonlinear Systems With Spline Networks and Outliers DOI
Xuehai Wang,

Sirui Zhao

International Journal of Robust and Nonlinear Control, Год журнала: 2025, Номер unknown

Опубликована: Май 9, 2025

ABSTRACT Nonlinear system identification plays a key role in real‐world modeling. The spline networks can model the nonlinearity with high precision without prior knowledge of nonlinear structure. This paper examines problem Hammerstein systems outliers by using to describe nonlinearity. To avoid redundant computation, two sub‐models are derived, one local parameters and other global linear parameters. By exploiting insensitivity correntropy outliers, correntropy‐based robust interval‐varying recursive estimation method is presented. proposed not only models unknown computational efficiency but also under premise that total distribution observed data unknown. superiority algorithm validated simulation experiments.

Язык: Английский

Highly efficient three-stage maximum likelihood recursive least squares identification method for multiple-input multiple-output systems DOI
Huihui Wang, Ximei Liu

Systems & Control Letters, Год журнала: 2025, Номер 200, С. 106094 - 106094

Опубликована: Апрель 6, 2025

Язык: Английский

Процитировано

2
Hierarchical Newton iterative identification methods for a class of input multi-piecewise Hammerstein models with autoregressive noise DOI
Yamin Fan, Ximei Liu, Meihang Li

и другие.

Mathematics and Computers in Simulation, Год журнала: 2025, Номер 237, С. 247 - 262

Опубликована: Апрель 25, 2025

Язык: Английский

Процитировано

1
Correntropy‐Based Robust Interval‐Varying Recursive Estimation Method for Nonlinear Systems With Spline Networks and Outliers DOI
Xuehai Wang,

Sirui Zhao

International Journal of Robust and Nonlinear Control, Год журнала: 2025, Номер unknown

Опубликована: Май 9, 2025

ABSTRACT Nonlinear system identification plays a key role in real‐world modeling. The spline networks can model the nonlinearity with high precision without prior knowledge of nonlinear structure. This paper examines problem Hammerstein systems outliers by using to describe nonlinearity. To avoid redundant computation, two sub‐models are derived, one local parameters and other global linear parameters. By exploiting insensitivity correntropy outliers, correntropy‐based robust interval‐varying recursive estimation method is presented. proposed not only models unknown computational efficiency but also under premise that total distribution observed data unknown. superiority algorithm validated simulation experiments.

Язык: Английский

Процитировано

0