Premium
GLOBAL CONVERGENCE OF MINIMUM VARIANCE SELF‐TUNING SCHEME BASED UPON DAMPED LEAST SQUARES
Author(s) -
Chen ZengQiang,
Lin MaoQiong,
Yuan ZhuZhi
Publication year - 2000
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1111/j.1934-6093.2000.tb00145.x
Subject(s) - convergence (economics) , least squares function approximation , identification (biology) , mathematics , stability (learning theory) , control theory (sociology) , recursive least squares filter , variance (accounting) , explained sum of squares , self tuning , total least squares , generalized least squares , non linear least squares , adaptive control , identification scheme , system identification , scheme (mathematics) , mathematical optimization , computer science , algorithm , control (management) , adaptive filter , statistics , engineering , regression , mathematical analysis , data modeling , artificial intelligence , control engineering , pid controller , process (computing) , database , estimator , business , economic growth , temperature control , operating system , accounting , economics , biology , machine learning , botany
This paper presents an analysis of the stability and convergence of a damped least squares identification algorithm and establishes the global convergence of a minimum variance self‐tuning scheme based upon damped least squares. The results mathematically demonstrate that the damped least squares can generally be applied to achieve system identification and adaptive control.