Data filtering based least squares iterative algorithm for parameter identification of output error autoregressive systems-Reference-Cited by-同舟云学术

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Data filtering based least squares iterative algorithm for parameter identification of output error autoregressive systems

Published:2014-10 Issue:10 Volume:114 Page:573-578
ISSN:0020-0190
Container-title:Information Processing Letters
language:en
Short-container-title:Information Processing Letters

Author:

Chen Huibo,Zhang Wenge,Ding Feng

Funder

Priority Academic Program Development of Jiangsu Higher Education Institutions

Publisher

Elsevier BV

Subject

Computer Science Applications,Information Systems,Signal Processing,Theoretical Computer Science

Reference35 articles.

1. Stabilization of networked control systems with random delays;Luan;IEEE Trans. Ind. Electron.,2011

2. H-infinity control for discrete-time Markov jump systems with uncertain transition probabilities;Luan;IEEE Trans. Autom. Control,2013

3. H-infinity filtering for discrete-time systems with stochastic incomplete measurement and mixed delays;Shi;IEEE Trans. Ind. Electron.,2012

4. System Identification;Söderström,1989

5. System Identification: Theory for the User;Ljung,1999

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1. An Improved Method for Stochastic Nonlinear System’s Identification Using Fuzzy-Type Output-Error Autoregressive Hammerstein–Wiener Model Based on Gradient Algorithm, Multi-Innovation, and Data Filtering Techniques;Complexity;2021-08-20

2. Identification of Hammerstein systems with continuous nonlinearity;Information Processing Letters;2015-11

3. Data Filtering-Based Multi-innovation Stochastic Gradient Algorithm for Nonlinear Output Error Autoregressive Systems;Circuits, Systems, and Signal Processing;2015-05-09

4. Recursive least squares parameter identification algorithms for systems with colored noise using the filtering technique and the auxilary model;Digital Signal Processing;2015-02

5. Multi-innovation parameter estimation for Hammerstein MIMO output-error systems based on the key-term separation∗∗This work was supported by the National Natural Science Foundation of China and the PAPD of Jiangsu Higher Education Institutions.;IFAC-PapersOnLine;2015

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