Open AccessPerformance analysis of the generalised projection identification for time‐varying systems
Author(s) -
Ding Feng,
Xu Ling,
Zhu Quanmin
Publication year - 2016
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2016.0202
Subject(s) - projection (relational algebra) , algorithm , estimation theory , convergence (economics) , system identification , mathematics , noise (video) , least squares function approximation , identification (biology) , recursive least squares filter , stochastic process , mathematical optimization , computer science , control theory (sociology) , adaptive filter , data modeling , statistics , artificial intelligence , database , estimator , economics , image (mathematics) , economic growth , botany , control (management) , biology
The least mean square methods include two typical parameter estimation algorithms, which are the projection algorithm and the stochastic gradient algorithm, the former is sensitive to noise and the latter is not capable of tracking the time‐varying parameters. On the basis of these two typical algorithms, this study presents a generalised projection identification algorithm (or a finite data window stochastic gradient identification algorithm) for time‐varying systems and studies its convergence by using the stochastic process theory. The analysis indicates that the generalised projection algorithm can track the time‐varying parameters and requires less computational effort compared with the forgetting factor recursive least squares algorithm. The way of choosing the data window length is stated so that the minimum parameter estimation error upper bound can be obtained. The numerical examples are provided.