Hierarchical multi-innovation stochastic gradient identification algorithm for estimating a bilinear state-space model with moving average noise
type: Item
instance of: scholarly article
title: Hierarchical multi-innovation stochastic gradient identification algorithm for estimating a bilinear state-space model with moving average noise
zbMATH Open document ID: 1503.93046
author: Yanyan Li
published in: Journal of Computational and Applied Mathematics
publication date: 2022-10-21T00:00:00Z
review text: This article considers simultaneous state and parameter estimation of a bilinear control system with linear observations corrupted by a noise that is the output of a rational transfer function fed by zero mean uncorrelated noise. A state estimator is derived based on Kalman filter and then a parameter estimation scheme is proposed using a hierarchical decomposition and stochastic gradient descent, using multiple innovations processes. Consistency of this scheme is established. Supporting numerical experiments are reported.
Mathematics Subject Classification ID: 93E12
Mathematics Subject Classification ID: 93E11
Mathematics Subject Classification ID: 93E14
Mathematics Subject Classification ID: 93E10
Mathematics Subject Classification ID: 62M05
zbMATH DE Number: 7604630
zbMATH Keywords: hierarchical parameter estimation
zbMATH Keywords: bilinear control system
zbMATH Keywords: state estimation
zbMATH Keywords: stochastic gradient
MaRDI profile type: Publication
full work available at URL: j.cam.2022.114794
OpenAlex ID: W4293722677
Wikidata QID: Q114201629
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DOI: 10.1016/J.CAM.2022.114794
description: scientific article; zbMATH DE number 7604630