Multi-innovation extended stochastic gradient algorithm for multi-input multi-output controlled autoregressive moving average systems by using the filtering technique

In this paper, we extends the innovation vector to the innovation matrices and presents a filtering based multi-innovation extended stochastic gradient algorithm for multi-input multi-output controlled autoregressive moving average systems. The basic idea is using the filtering technique to transfor...

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Bibliographic Details
Published in:Proceedings of the American Control Conference pp. 925 - 929
Main Authors: Jiang, Xiao, Pan, Jian, Wan, Xiangkui, Ding, Feng
Format: Conference Proceeding Journal Article
Language:English
Published: American Automatic Control Council (AACC) 01.07.2016
Subjects:
Algorithms
Autoregressive moving average
Autoregressive processes
Conferences
Control systems
Filtering
Filtration
Innovation
Mathematical model
MIMO
Parameter estimation
Signal processing algorithms
Stochastic processes
Stochasticity
Technological innovation
ISSN:2378-5861
Online Access:Get full text
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Summary:In this paper, we extends the innovation vector to the innovation matrices and presents a filtering based multi-innovation extended stochastic gradient algorithm for multi-input multi-output controlled autoregressive moving average systems. The basic idea is using the filtering technique to transform a multivariable system into two identification models, then to identify the parameters of these two identification models interactively. The proposed multi-innovation identification algorithm can effectively improve the parameter estimation accuracy.
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ISSN:2378-5861
DOI:10.1109/ACC.2016.7525033