On the stability and convergence of a sliding-window variable-regularization recursive-least-squares algorithm-Reference-Cited by-同舟云学术

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On the stability and convergence of a sliding-window variable-regularization recursive-least-squares algorithm

Published:2015-10-07 Issue:5 Volume:30 Page:715-735
ISSN:0890-6327
Container-title:International Journal of Adaptive Control and Signal Processing
language:en
Short-container-title:Int. J. Adapt. Control Signal Process.

Author:

Ali Asad A.¹,Hoagg Jesse B.²,Mossberg Magnus³,Bernstein Dennis S.¹

Affiliation:

1. Department of Aerospace Engineering; University of Michigan; Ann Arbor MI 48109 USA

2. Department of Mechanical Engineering; University of Kentucky; Lexington KY 40506 USA

3. Department of Engineering and Physics; Karlstad University; SE-65188 Karlstad Sweden

Publisher

Wiley

Subject

Electrical and Electronic Engineering,Signal Processing,Control and Systems Engineering

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1002/acs.2634/fullpdf

Reference25 articles.

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Selective Memory Recursive Least Squares: Recast Forgetting Into Memory in RBF Neural Network-Based Real-Time Learning;IEEE Transactions on Neural Networks and Learning Systems;2024

2. Development of Block Iterated Extended Kalman Filter for Recursive Estimation of ARMAX Model Parameters;2023 Ninth Indian Control Conference (ICC);2023-12-18

3. A Recursive Least-Squares with a Time-Varying Regularization Parameter;Applied Sciences;2022-02-16

4. Development of Block Oriented Recursive and Constrained Parameter Estimation Schemes for ARMAX Models;IFAC-PapersOnLine;2022

5. Parameter estimation for time‐delay systems based on the frequency responses and harmonic balance methods;International Journal of Adaptive Control and Signal Processing;2020-09-23

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