A dynamical system approach to stochastic approximations

被引:122
作者
Benaim, M
机构
[1] Department of Mathematics, University of California at Berkeley, Berkeley
关键词
stochastic approximations; ordinary differential equations; chain-recurrence; neural networks;
D O I
10.1137/S0363012993253534
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
It is known that some problems of almost sure convergence for stochastic approximation processes can be analyzed via an ordinary differential equation (ODE) obtained by suitable averaging. The goal of this paper is to show that the asymptotic behavior of such a process can be related to the asymptotic behavior of the ODE without any particular assumption concerning the dynamics of this ODE. The main results are as follows: a) The limit sets of trajectory solutions to the stochastic approximation recursion are, under classical assumptions, almost surely nonempty compact connected sets invariant under the how of the ODE and contained in its set of chain-recurrence. b) If the gain parameter goes to zero at a suitable rate depending on the expansion rate of the ODE, any trajectory solution to the recursion is almost surely asymptotic to a forward trajectory solution to the ODE.
引用
收藏
页码:437 / 472
页数:36
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