Accession Number:

ADA175028

Title:

Asymptotic Properties of Distributed and Communicating Stochastic Approximation Algorithms,

Descriptive Note:

Corporate Author:

BROWN UNIV PROVIDENCE RI LEFSCHETZ CENTER FOR DYNAMICAL SYSTEMS

Personal Author(s):

Report Date:

1986-02-01

Pagination or Media Count:

58.0

Abstract:

The asymptotic properties of extensions of the type of distributed or decentralized stochastic approximation proposed are developed. Such algorithms have numerous potential applications in decentralized estimation, detection and adaptive control, or in decentralized Monte Carlo simulation for system optimization where they can exploit th possibilities of parallel processing. The structure involves several isolated processors recursive algorithms who communicate to each other asyhnchronously and at random intervals. The asymptotic small gain properties are derived. The communication intervals need not be strictly bounded and they and the system noise can depend on the communicating system state. State space constraints are also handled. In many applications, the dynamical terms are merely indicator functions, or have other types of discontinuities. The typical such case is also treated, as is the case where there is noise in the communication. The linear stochastic differential equation satisfied by the interpolated asymptotic normalized error sequence is derived, and issued to compare alternative algorithms and communication strategies. Weak convergence methods provide the basic tools.

Subject Categories:

  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE