Stochastic Approximation: Convergence Results for Dependent Observations.

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)

Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1974
Accession Number
AD0787842

Entities

People

  • David C. Farden
  • Louis L. Louis L. Scharf

Organizations

  • Colorado State University

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Convergence
  • Covariance
  • Data Science
  • Ergodic Processes
  • Estimators
  • Information Processing
  • Information Science
  • Mathematics
  • Observation
  • Sequences
  • Signal Processing
  • Statistical Algorithms
  • Statistical Analysis

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Mathematical Modeling and Probability Theory.