A network model for blind source extraction in various ill-conditioned cases

Yuanqing Li, Jun Wang

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

This paper discusses blind source extraction in various ill-conditioned cases based on a simple extraction network model. Extractability is first analyzed for the following ill-conditioned cases: the mixing matrix is square but singular, the number of sensors is smaller than that of sources, the number of sensors is larger than that of sources but the column rank of mixing matrix is deficient, and the number of sources is unknown and the column rank of mixing matrix is deficient. A necessary and sufficient condition for extractability is obtained. A cost function and an unsupervised learning algorithm for the extraction network model are developed. Simulation results are also presented to show the validity of the theoretical results and the performance and characteristics of the learning algorithm.

Original languageEnglish
Pages (from-to)1348-1356
Number of pages9
JournalNeural Networks
Volume18
Issue number10
DOIs
Publication statusPublished - Dec 2005
Externally publishedYes

Keywords

  • Adaptive algorithm
  • Cost function
  • Extractability
  • Ill-conditioned cases
  • Sequential blind extraction

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