Stochastic gradient algorithm based on an improved higher order exponentiated error cost function

Umair Bin Mansoor, Syed Muhammad Asad, Azzedine Zerguine

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

We propose stochastic gradient algorithm based on exponentiated cost functions that employ higher order moments of the chosen error. Recently, such algorithms based on exponential dependence of squared of the error have attracted a lot of attention. It has been felt that such algorithms have only been tested in the Gaussian noise environment. Motivated by the performance of the least-mean-fourth algorithm in sub-Gaussian environments, we make use of the same strategy to come up with a new algorithm with superior convergence and steady-state performance. Simulations show promising results.

Original languageEnglish
Title of host publicationConference Record of the 48th Asilomar Conference on Signals, Systems and Computers
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages900-903
Number of pages4
ISBN (Electronic)9781479982974
DOIs
StatePublished - 24 Apr 2015

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2015-April
ISSN (Print)1058-6393

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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