Recursive identification for Wiener non-linear systems with non-stationary disturbances

This study investigates the robust recursive identification problem for Wiener non-linear systems with non-stationary disturbance and measurement white Gaussian noise. Non-stationary disturbances cannot be eliminated by conventional statistical strategy. To overcome such problem, the non-stationary...

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Bibliographic Details
Published in:	IET control theory & applications Vol. 13; no. 16; pp. 2648 - 2657
Main Authors:	Dong, Shijian, Yu, Li, Zhang, Wen-An, Chen, Bo
Format:	Journal Article
Language:	English
Published:	The Institution of Engineering and Technology 05.11.2019
Subjects:	adaptive control asymptotic convergence conditions covariance matrices covariance matrix discrete time systems discrete‐time recursive least‐squares algorithm dynamic tracking ability filtering theory Gaussian noise identification information vector least squares approximations measurement white Gaussian noise nonlinear control systems nonstationary disturbance signal parameter estimation parameter vector persistent excitation condition recursive estimation Research Article robust recursive identification problem statistical analysis steady asymptotic convergence stochastic processes stochastic statistical theory time‐invariant system parameters white noise Wiener nonlinear systems steady asymptotic convergence Wiener nonlinear systems stochastic statistical theory robust recursive identification problem covariance matrix white noise identification measurement white Gaussian noise Gaussian noise time-invariant system parameters recursive estimation parameter estimation stochastic processes covariance matrices least squares approximations nonstationary disturbance signal discrete time systems dynamic tracking ability filtering theory asymptotic convergence conditions adaptive control discrete-time recursive least-squares algorithm parameter vector nonlinear control systems information vector statistical analysis persistent excitation condition
ISSN:	1751-8644, 1751-8652
Online Access:	Get full text
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