Accession Number:

AD0787842

Title:

Stochastic Approximation: Convergence Results for Dependent Observations.

Descriptive Note:

Technical rept.,

Corporate Author:

COLORADO STATE UNIV FORT COLLINS DEPT OF ELECTRICAL ENGINEERING

Personal Author(s):

Report Date:

1974-10-01

Pagination or Media Count:

23.0

Abstract:

Robbins-Monro stochastic approximation algorithms arise in many single- and multi-sensor signal processing applications where there is a need to adapt to unknown statistical parameters. In this report a theorem is stated and proved that ensures almost sure a.s. convergence of the Robbins-Monro algorithm provided the observation sequence satisfies certain covariance ergodicity conditions. These conditions are related to the conditions required to obtain a.s. convergence of the usual covariance estimator. Author

Subject Categories:

  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE