Journal ArticleDOI
Parameter identification of Wiener systems with multisegment piecewise-linear nonlinearities
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TLDR
The proposed iterative method enables simultaneous estimation of both the linear block parameters and all the parameters characterizing the nonlinearity, i.e., the slopes of linear segments and the constants determining the partition of domain.About:
This article is published in Systems & Control Letters.The article was published on 2007-02-01. It has received 158 citations till now. The article focuses on the topics: Piecewise linear function & Wiener process.read more
Citations
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Journal ArticleDOI
Identification methods for Hammerstein nonlinear systems
TL;DR: A Newton recursive and a Newton iterative identification algorithms are derived by using the Newton method (Newton-Raphson method) to reduce the sensitivity of the projection algorithm to noise, and to improve convergence rates of the SG algorithm.
Journal ArticleDOI
Gradient-based and least-squares-based iterative algorithms for Hammerstein systems using the hierarchical identification principle
Feng Ding,Xinggao Liu,Jian Chu +2 more
TL;DR: The simulation results confirm that the proposed two algorithms can give satisfactory identification accuracies and the least-squares-based iterative algorithm has faster convergence rates than the gradient-basedIterative algorithm.
Journal ArticleDOI
Least squares based and gradient based iterative identification for Wiener nonlinear systems
Dongqing Wang,Feng Ding +1 more
TL;DR: This paper derives a least squares-based and a gradient-based iterative identification algorithms for Wiener nonlinear systems, estimating directly the parameters of Wiener systems without re-parameterization to generate redundant estimates.
Journal ArticleDOI
Multiinnovation Least-Squares Identification for System Modeling
TL;DR: A new interval-varying MILS algorithm is proposed, for which the key is to dynamically change the interval in order to deal with cases where some measurement data are missing, and an auxiliary-model-based MILs algorithm is derived for pseudolinear models corresponding to output error moving average systems with colored noises.
Journal ArticleDOI
Parameter estimation for block‐oriented nonlinear systems using the key term separation
TL;DR: A key term separation auxiliary model three‐stage gradient‐based iterative (KT‐AM‐3S‐GI) identification algorithm is proposed by using the hierarchical identification principle and the simulation results confirm the effectiveness of the proposed algorithm.
References
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Journal ArticleDOI
Recursive prediction error identification using the nonlinear Wiener model
TL;DR: A recursive prediction error identification algorithm, based on the Wiener model, is derived and shows that the input signal should be such that there is signal energy in the whole range of the piecewise linear approximation.
Journal ArticleDOI
Gray-box identification of block-oriented nonlinear models
R. K. Pearson,Martin Pottmann +1 more
TL;DR: In this article, a gray-box identification approach to three classes of block-oriented models, namely Hammerstein models, Wiener models, and feedback blockoriented models with output multiplicities, is presented.
Book
Nonlinear System Identification : Input-output Modeling Approach
Robert Haber,László Keviczky +1 more
Journal ArticleDOI
Model predictive control based on Wiener models
TL;DR: In this paper, the authors examined various model structures including ARX and step-response models with polynomial or spline nonlinearities and their corresponding identification strategies and compared the performance of the Wiener MPC with that of the linear MPC and the benchmark PID control.