A realization approach to stochastic model reduction
作者:
UDAYB. DESAI,
DEBAJYOTI PAL,
ROBERTD. KIRKPATRICK,
期刊:
International Journal of Control
(Taylor Available online 1985)
卷期:
Volume 42,
issue 4
页码: 821-838
ISSN:0020-7179
年代: 1985
DOI:10.1080/00207178508933398
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
The problem of discrete-time stochastic model reduction (approximation) is considered. Using the canonical correlation analysis approach of Akaike (1975), a new order-reduction algorithm is developed. Furthermore, it is shown that the inverse of the reduced-order realization is asymptotically stable. Next, an explicit relationship between canonical variables and the linear least-squares estimate of the state vector is established. Using this, a more direct approach for order reduction is presented, and also a new design for reduced-order Kalman filters is developed. Finally, the uniqueness and symmetry properties for the new realization—the balanced stochastic realization—along with a simulation result, are presented.
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