A new algorithm for linear system identification
IEEE Transactions on Automatic ControlPublished 1 October 1968
G.N. Saridis, G. Stein
Citations40
SJR quartileQ1
SJR score3.80
SNIP2.59
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Abstract
This correspondence considers the on-line parameter identification of a forced linear discrete-time dynamic system from a sequence of white-noise-corrupted output measurements. In contrast to other approaches, the proposed stochastic approximation algorithm does not require knowledge of the noise statistics and converges to the true value of the parameters in the mean-square sense. If the input measurements are also corrupted with white noise, an additional term depending on the variance of the noise is required.
Keywords
Computer ScienceEngineering
ON STOCHASTIC APPROXIMATION
325 Citations1956A. Dvoretzky
Stochastic approximation algorithms for linear discrete-time system identification
7 Citations1967G.N. Saridis, G. Stein
A generalized algorithm for on-line identification of a stochastic linear discrete-time system using noisy input and output measurements is presented and shown to converge in the mean-square sense.