Particle filtering-based recursive identification for controlled auto-regressive systems with quantised output

Recursive prediction error method is one of the main tools for analysis of controlled auto-regressive systems with quantised output. In this study, a recursive identification algorithm is proposed based on the auxiliary model principle by modifying the standard stochastic gradient algorithm. To impr...

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Bibliographic Details
Published in:	IET control theory & applications Vol. 13; no. 14; pp. 2181 - 2187
Main Authors:	Ding, Jie, Chen, Jiazhong, Lin, Jinxing, Jiang, Guoping
Format:	Journal Article
Language:	English
Published:	The Institution of Engineering and Technology 24.09.2019
Subjects:	autoregressive processes auxiliary model principle auxiliary model‐based controlled auto‐regressive systems discrete random sampling points filtering theory gradient methods invalid particles least squares approximations linear output estimates main tools novel particle filtering technique novel recursive identification algorithm parameter estimation particle filtering (numerical methods) particle filtering technique‐based algorithm particle filtering‐based recursive identification posterior probability density function probability quantised output recursive estimation recursive prediction error method Research Article standard stochastic gradient algorithm stochastic processes linear output estimates least squares approximations controlled auto-regressive systems posterior probability density function standard stochastic gradient algorithm particle filtering technique-based algorithm filtering theory probability particle filtering (numerical methods) novel particle filtering technique autoregressive processes novel recursive identification algorithm quantised output recursive prediction error method recursive estimation parameter estimation main tools invalid particles auxiliary model-based stochastic processes gradient methods discrete random sampling points particle filtering-based recursive identification auxiliary model principle
ISSN:	1751-8644, 1751-8652
Online Access:	Get full text
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