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A new filter‐based stochastic gradient algorithm for dual‐rate ARX models
Author(s) -
Chen Jing,
Liu Yanjun,
Xu Ling
Publication year - 2018
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.2930
Subject(s) - kalman filter , dual (grammatical number) , algorithm , filter (signal processing) , computer science , control theory (sociology) , extended kalman filter , mathematics , mathematical optimization , artificial intelligence , art , literature , control (management) , computer vision
Summary This paper proposes a new filter‐based stochastic gradient algorithm for dual‐rate ARX models. Algorithm analysis is based upon the Kalman filter and smoother method. The new filter applies the measurable outputs to adjust the estimated outputs during each interval of the slow sampled rate. A comparative study reveals that the present consideration makes the estimated outputs more accurate than the classical auxiliary model. A stochastic gradient algorithm is developed for the estimation of parameters using all data. The simulation made further guarantees the usefulness of the proposed algorithm.