An Averaging Result for Random Differential Equations

Abstract

This paper concerns differential equations which contain strong mixing random processes. The solution process is shown to be well approximated by a deterministic trajectory, over an infinite time interval, using the interplay between the rate of fluctuations of the random process and the rate of the psi mixing. An application of the result is given for analyzing synaptic modifications in Neural Networks. (kr)

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Document Details

Document Type
Technical Report
Publication Date
Apr 12, 1990
Accession Number
ADA220364

Entities

People

  • Nathan Intrator

Organizations

  • Brown University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Applied Mathematics
  • Convergence
  • Differential Equations
  • Dimensionality Reduction
  • Equations
  • Feature Extraction
  • Information Processing
  • Information Systems
  • Markov Processes
  • Neural Networks
  • New York
  • Probability
  • Stationary Processes
  • Stochastic Processes
  • Time Intervals
  • United States

Fields of Study

  • Mathematics

Readers

  • Calculus or Mathematical Analysis
  • Neuroscience
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks