Auxiliary model based multi-innovation extended stochastic gradient parameter estimation with colored measurement noises
type: Item
instance of: scholarly article
title: Auxiliary model based multi-innovation extended stochastic gradient parameter estimation with colored measurement noises
zbMATH Open document ID: 1178.94137
author: Feng Ding
author: Guangjun Liu
author: Xiaoping Liu
published in: Signal Processing
publication date: 2009-10-26T00:00:00Z
Mathematics Subject Classification ID: 94A13
Mathematics Subject Classification ID: 93E10
Mathematics Subject Classification ID: 62F99
zbMATH DE Number: 5620489
zbMATH Keywords: parameter estimation
zbMATH Keywords: multi-innovation identification theory
zbMATH Keywords: LMS
zbMATH Keywords: stochastic gradient
zbMATH Keywords: output error models
zbMATH Keywords: output error moving average (OEMA) models
MaRDI profile type: Publication
full work available at URL: j.sigpro.2009.03.020
OpenAlex ID: W2053863904
cites work: Some further results on blind identification of MIMO FIR channels via second-order statistics
cites work: Estimation of the parameters of multichannel autoregressive signals from noisy observations
cites work: Variable step-size LMS algorithm with a quotient form
cites work: Q3799870
cites work: Performance analysis of multi-innovation gradient type identification methods
cites work: Multi-innovation least squares identification methods based on the auxiliary model for MISO systems
cites work: Q5320072
cites work: Self-tuning control based on multi-innovation stochastic gradient parameter estimation
cites work: Identification for multirate multi-input systems using the multi-innovation identification theory
cites work: Q4263623
cites work: Recursive output error identification algorithms theory and evaluation
cites work: Analysis of an output error identification algorithm
cites work: Combined parameter and output estimation of dual-rate systems using an auxiliary model
cites work: Identification of dual-rate systems based on finite impulse response models
cites work: Reconstruction of continuous-time systems from their non-uniformly sampled discrete-time systems
cites work: Extended stochastic gradient identification algorithms for Hammerstein-Wiener ARMAX systems
DOI: 10.1016/J.SIGPRO.2009.03.020
description: scientific article; zbMATH DE number 5620489