Convergence analysis of adaptive total least square based on deterministic discrete time

Xiao Bo Li, Yang Yu Fan, Ke Peng

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Deterministic continuous time (DCT) is a conventional method of studying the minor component analysis (MCA). Unfortunately, DCT is not used in practical systems because of it's strict conditions. Therefore, the convergence condition of AMEX MCA learning algorithm is derived based on deterministic discrete time. Theoretical analysis shows that the total least square solution is not obtained until special conditions between learning factor and autocorrelation matrix of input signal are satisfied. Finally, simulation results show the correctness of the convergence condition.

Original languageEnglish
Pages (from-to)1399-1402
Number of pages4
JournalKongzhi yu Juece/Control and Decision
Volume25
Issue number9
StatePublished - Sep 2010

Keywords

  • Deterministic discrete time
  • Minor component analysis
  • Total least square

Fingerprint

Dive into the research topics of 'Convergence analysis of adaptive total least square based on deterministic discrete time'. Together they form a unique fingerprint.

Cite this