Convergence and stability of recursive damped least square algorithm
type: Item
instance of: scholarly article
title: Convergence and stability of recursive damped least square algorithm
zbMATH Open document ID: 0980.93084
DOI: 10.1007/BF02458525
author: Maoqiong Lin
author: Zengqiang Chen
author: Zhuzhi Yuan
published in: Applied Mathematics and Mechanics. (English Edition)
publication date: 2002-03-12T00:00:00Z
review text: A convergence analysis for a new identification algorithm (recursive damped least squares) is given. It is shown that under persistent excitation conditions, the parametric distance converges to a zero mean and bounded variance random variable. Under the same conditions, the condition number of the adaptation gain matrix is bounded and the variance of the parametric distance is bounded. The results are formulated in two theorems. The proof of theorem 1 is omitted, theorem 2 is completely proved.
Mathematics Subject Classification ID: 93E24
Mathematics Subject Classification ID: 93E12
zbMATH DE Number: 1490126
zbMATH Keywords: recursive damped least squares
zbMATH Keywords: identification
zbMATH Keywords: persistent excitation
zbMATH Keywords: parametric distance
zbMATH Keywords: condition number
reviewed by: Werner H. Schmidt
MaRDI profile type: Publication
cites work: Q3799870
cites work: Convergence analysis of recursive identification algorithms with forgetting factor
cites work: Analysis of a general recursive prediction error identification algorithm
cites work: Improved least squares identification
cites work: A method for the solution of certain non-linear problems in least squares
full work available at URL: https://doi.org/10.1007/bf02458525
OpenAlex ID: W2020922841
description: scientific article; zbMATH DE number 1490126