Journal ArticleDOI
Recursive least squares algorithm and gradient algorithm for Hammerstein–Wiener systems using the data filtering
Yanjiao Wang,Feng Ding +1 more
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TLDR
The proposed algorithms can effectively estimate the parameters of Hammerstein–Wiener systems and the computational efficiency of the proposed algorithms is analyzed and compared.Abstract:
This paper considers the parameter estimation problems of Hammerstein–Wiener systems by using the data filtering technique. In order to improve the estimation accuracy, the data filtering-based recursive generalized extended least squares algorithm is derived. In order to improve the computational efficiency, the data filtering-based generalized extended stochastic gradient algorithm is derived for estimating the system parameters. Finally, the computational efficiency of the proposed algorithms is analyzed and compared. The simulation results indicate that the proposed algorithms can effectively estimate the parameters of Hammerstein–Wiener systems.read more
Citations
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The parameter estimation algorithms based on the dynamical response measurement data
TL;DR: In this article, the authors studied the parameter estimation to the system response from the discrete measurement data, by constructing the dynamical rolling cost functions and using the nonlinear optimization, t
Journal ArticleDOI
Decomposition based least squares iterative identification algorithm for multivariate pseudo-linear ARMA systems using the data filtering
TL;DR: A decomposition based least squares iterative identification algorithm for multivariate pseudo-linear autoregressive moving average systems using the data filtering to transform the original system to a hierarchical identification model and to decompose this model into three subsystems and to identify each subsystem.
Journal ArticleDOI
Recursive Least Squares and Multi-innovation Stochastic Gradient Parameter Estimation Methods for Signal Modeling
TL;DR: This paper studies the parameter estimation problem for the sine combination signals and periodic signals and presents the multi-innovation stochastic gradient parameter estimation method, derived by means of the trigonometric function expansion.
Journal ArticleDOI
Parameter estimation algorithms for dynamical response signals based on the multi-innovation theory and the hierarchical principle
TL;DR: A hierarchical identification algorithm is derived by means of the decomposition technique and interaction estimation theory and a multi-innovation stochastic gradient algorithm is proposed through expanding the scalar innovation into an innovation vector in order to obtain more accurate parameter estimates.
Journal ArticleDOI
A multi-innovation state and parameter estimation algorithm for a state space system with d-step state-delay
TL;DR: This work extends the scalar innovation into an innovation vector and presents a multi-innovation gradient parameter estimation algorithm for a state-space system with d-step state-delay by means of the multi-invention identification theory.
References
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Book
System Identification: Theory for the User
TL;DR: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis and praktische Anwendung der verschiedenen Verfahren zur IdentifIZierung hat.
Book
Adaptive filtering prediction and control
Graham C. Goodwin,Kwai Sang Sin +1 more
TL;DR: This unified survey focuses on linear discrete-time systems and explores the natural extensions to nonlinear systems and summarizes the theoretical and practical aspects of a large class of adaptive algorithms.
Journal ArticleDOI
$H_{\infty}$ Filtering for Discrete-Time Systems With Stochastic Incomplete Measurement and Mixed Delays
Peng Shi,Xiaoli Luan,Fei Liu +2 more
TL;DR: A more realistic and accurate measurement mode is proposed to compensate for the negative influence of both missing data and different time delays in a random way to ensure the asymptotic stability as well as a prescribed H∞ performance for the filter errors.
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