Discrete-Time Minimum Tracking Based on Stochastic Approximation Algorithm With Randomized Differences

O. Granichin, L. Gurevich, A. Vakhitov

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

20 Scopus citations

Abstract

In this paper application of the stochastic approximation algorithm with randomized differences to the minimum tracking problem for the non-constrained optimization is considered. The upper bound of mean-squared estimation error is derived in the case of once differentiable functional and almost arbitrary observation noise. Numerical simulation of the estimates stabilization for the multidimensional optimization with unknown but bounded deterministic noise is provided. Stabilization bound has sufficiently small level comparing to significant level of noise.

Original languageEnglish
Title of host publicationPROCEEDINGS OF THE 48TH IEEE CONFERENCE ON DECISION AND CONTROL, 2009 HELD JOINTLY WITH THE 2009 28TH CHINESE CONTROL CONFERENCE (CDC/CCC 2009)
PublisherIEEE Canada
Pages5763-5767
Number of pages5
DOIs
StatePublished - 2009
EventJoint 48th IEEE Conference on Decision and Control (CDC) / 28th Chinese Control Conference (CCC) - Shanghai
Duration: 15 Dec 200918 Dec 2009

Publication series

NameProceedings of the IEEE Conference on Decision and Control
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISSN (Print)0191-2216

Conference

ConferenceJoint 48th IEEE Conference on Decision and Control (CDC) / 28th Chinese Control Conference (CCC)
CityShanghai
Period15/12/0918/12/09

Keywords

  • NOISE

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