Stochastic approximation algorithms for identifying ARMA processes
作者:
DANIEL GRAUPE,
JOSEPH PERL,
期刊:
International Journal of Systems Science
(Taylor Available online 1974)
卷期:
Volume 5,
issue 11
页码: 1025-1028
ISSN:0020-7721
年代: 1974
DOI:10.1080/00207727408920158
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
Following the convergence proofs for stochastic approximation identification of pure autoregressive (AR) processes with dependent observations, as derived by Saridis and Stein, it is shown that the convergence for mixed autoregressive-moving-average (ARMA) cases can also be proved when none of the AR or the MA parameters or of the covariances are assumed known. Consequently, a generalized stochastic approximations identification procedure for ARMA processes is derived, which is extendable to any linear Kalrman filter models.
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