Unifying view of stochastic approximation, Kalman filter and backpropagation

Research output: Contribution to conferencePaperpeer-review

3 Scopus citations

Abstract

In this paper the relationships between the Stochastic Approximation, the Kalman Filter and the Backpropagation algorithms are investigated. We show that when the Neural Network architecture at hand can be formalized such that the approximation of the optimum for a nonlinear objective function is the problem for which we seek a solution, then both Stochastic Approximation techniques and appropriate Kalman Filters can be employed in order to reach the goal but the latter can also handle various structural characteristics of the stochastic processes involved and suggest a more efficient two-step estimator.

Original languageEnglish (US)
Pages87-94
Number of pages8
StatePublished - 1995
Externally publishedYes
EventProceedings of the 5th IEEE Workshop on Neural Networks for Signal Processing (NNSP'95) - Cambridge, MA, USA
Duration: Aug 31 1995Sep 2 1995

Other

OtherProceedings of the 5th IEEE Workshop on Neural Networks for Signal Processing (NNSP'95)
CityCambridge, MA, USA
Period8/31/959/2/95

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Unifying view of stochastic approximation, Kalman filter and backpropagation'. Together they form a unique fingerprint.

Cite this