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Strong consistency of recursive identification by no use of persistent excitation condition

Strong consistency of recursive identification by no use of persistent excitation condition For the discrete-time stochastic system without monitoring, the strong consistency of the estimate given by the stochastic gradient algorithm is considered when the persistent excitation condition is possibly not fulfilled. In addition, the convergence rate is given for a specific class of system noises, while in the adaptive tracking case the convergence rate for the parameter estimates as well as the tracking error are obtained when the reference signal is disturbed by a “dither”. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Acta Mathematicae Applicatae Sinica Springer Journals

Strong consistency of recursive identification by no use of persistent excitation condition

Acta Mathematicae Applicatae Sinica , Volume 2 (2) – Apr 6, 2005

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Publisher
Springer Journals
Copyright
Copyright © 1985 by Science Press and D. Reidel Publishing Company
Subject
Mathematics; Applications of Mathematics; Math Applications in Computer Science; Theoretical, Mathematical and Computational Physics
ISSN
0168-9673
eISSN
1618-3932
DOI
10.1007/BF01539484
Publisher site
See Article on Publisher Site

Abstract

For the discrete-time stochastic system without monitoring, the strong consistency of the estimate given by the stochastic gradient algorithm is considered when the persistent excitation condition is possibly not fulfilled. In addition, the convergence rate is given for a specific class of system noises, while in the adaptive tracking case the convergence rate for the parameter estimates as well as the tracking error are obtained when the reference signal is disturbed by a “dither”.

Journal

Acta Mathematicae Applicatae SinicaSpringer Journals

Published: Apr 6, 2005

References