Optimal Convergence Rate of the Randomized Algorithms of Stochastic Approximation in Arbitrary Noise

被引：0

作者：

O. N. Granichin

机构：

[1] St. Petersburg State University,

来源：

Automation and Remote Control | 2003年 / 64卷

关键词：

Mechanical Engineer; System Theory; Convergence Rate; Unknown Parameter; Technical System;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Multidimensional stochastic optimization plays an important role in analysis and control of many technical systems. To solve the challenging multidimensional problems of optimization, it was suggested to use the randomized algorithms of stochastic approximation with perturbed input which are not just simple, but also provide consistent estimates of the unknown parameters for observations in “almost arbitrary” noise. Optimal methods of choosing the parameters of algorithms were motivated.

引用

页码：252 / 262

页数：10

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