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Maximum likelihood gradient identification for multivariate equation‐error moving average systems using the multi‐innovation theory
Author(s) -
Liu Lijuan,
Ding Feng,
Hayat Tasawar
Publication year - 2019
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.3007
Subject(s) - multivariate statistics , estimation theory , identification (biology) , maximum likelihood , mathematics , maximum likelihood sequence estimation , multivariate analysis , mathematical optimization , statistics , computer science , algorithm , botany , biology
Summary For the multivariate equation‐error moving average system, a multivariate maximum likelihood multi‐innovation extended stochastic gradient (M‐ML‐MIESG) algorithm is delivered. The key is to decompose the system into several regressive identification subsystems according to the number of the system outputs. Then, a multivariate maximum likelihood extended stochastic gradient algorithm is presented to estimate the parameters of these subsystems. The M‐ML‐MIESG algorithm has higher parameter estimation accuracy than the multivariate extended stochastic gradient algorithm. The simulation examples indicate that the proposed methods work well.