A prediction-error-method for recursive identification of nonlinear systems
type: Item
instance of: scholarly article
title: A prediction-error-method for recursive identification of nonlinear systems
zbMATH Open document ID: 0617.93066
DOI: 10.1016/0005-1098(87)90078-1
author: B. George
published in: Automatica
publication date: 1987-01-01T00:00:00Z
review text: Nonlinear systems can be identified with nonlinear filters by combined estimation of parameters and state. Often these methods are very complex or have convergence problems, as for example the Extended Kalman Filter. In this paper a new algorithm for recursive nonlinear system identification is presented. Convergence problems are eliminated by an improved calculation of the gradient as the total derivative of the prediction error. By separately estimating parameters and states, computation time is reduced. The efficiency of the new identification algorithm is illustrated by studying its performance in a gravimetric filling system.
Mathematics Subject Classification ID: 93E12
Mathematics Subject Classification ID: 93C10
Mathematics Subject Classification ID: 93E25
Mathematics Subject Classification ID: 62M20
Mathematics Subject Classification ID: 93E11
zbMATH DE Number: 4002007
zbMATH Keywords: bias reduction
zbMATH Keywords: combined estimation of parameters and state
zbMATH Keywords: Extended Kalman Filter
zbMATH Keywords: algorithm
zbMATH Keywords: recursive nonlinear system identification
MaRDI profile type: Publication
full work available at URL: 0005-1098(87)90078-1
OpenAlex ID: W2000612710
cites work: Q3993534
cites work: Comparison of Gradient Methods for the Solution of Nonlinear Parameter Estimation Problems
cites work: Recursive self-tuning algorithm for adaptive Kalman filtering
cites work: Q3799870
cites work: Correction to: A unified sequential identification structure based on convergence considerations
cites work: Input-output parametric models for non-linear systems Part II: stochastic non-linear systems
cites work: Convergence of an adaptive filter algorithm
cites work: Asymptotic behavior of the extended Kalman filter as a parameter estimator for linear systems
cites work: The simultaneous on-line estimation of parameters and states in linear systems
cites work: A modified extended Kalman filter for linear discrete-time systems with unknown parameters
description: scientific article; zbMATH DE number 4002007