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

被引：20
|
作者：
Granichin, Oleg [1 ]
Gurevich, Lev [1 ]
Vakhitov, Alexander [1 ]
机构：
[1] St Petersburg State Univ, Dept Math & Mech, St Petersburg 198504, Russia
来源：
PROCEEDINGS OF THE 48TH IEEE CONFERENCE ON DECISION AND CONTROL, 2009 HELD JOINTLY WITH THE 2009 28TH CHINESE CONTROL CONFERENCE (CDC/CCC 2009) | 2009年
关键词：
NOISE;
D O I：
10.1109/CDC.2009.5400839
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
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页码：5763 / 5767
页数：5
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