Cost function adaptation: a stochastic gradient algorithm for data echo cancellation

C. Rusu, Colin Cowan

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

A family of stochastic gradient algorithms and their behaviour in the data echo cancellation work platform are presented. The cost function adaptation algorithms use an error exponent update strategy based on an absolute error mapping, which is updated at every iteration. The quadratic and nonquadratic cost functions are special cases of the new family. Several possible realisations are introduced using these approaches. The noisy error problem is discussed and the digital recursive filter estimator is proposed. The simulation outcomes confirm the effectiveness of the proposed family of algorithms.
Original languageEnglish
Pages (from-to)516-526
Number of pages11
JournalIEE PROCEEDINGS-VISION IMAGE AND SIGNAL PROCESSING
Volume147
Issue number6
Publication statusPublished - Dec 2000

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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