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Stochastic average gradient algorithm for multirate FIR models with varying time delays using self‐organizing maps
Author(s) -
Chen Jing,
Shen Qianyan,
Ma Junxia,
Liu Yanjun
Publication year - 2020
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.3116
Subject(s) - algorithm , convergence (economics) , sampling (signal processing) , finite impulse response , impulse (physics) , computer science , instant , control theory (sociology) , mathematics , filter (signal processing) , artificial intelligence , physics , control (management) , quantum mechanics , economics , computer vision , economic growth
Summary A stochastic average gradient (SAG) algorithm is proposed for multirate (MR) finite impulse response (FIR) models with varying time delays in this article. The time delays at each sampling instant are computed through the self‐organizing maps technique, and then the parameters are estimated by using the SAG algorithm. Considering that the SAG algorithm updates the parameters using all the directions up to and including the current sampling instant, but only compute one gradient at each sampling instant, thus it has less computational efforts and quicker convergence rates. Furthermore, some modified SAG algorithms are also developed. Two simulation examples show that these algorithms identify MR FIR models with varying time delays correctly.