Least absolute deviation neural network and its application to time delay estimation

Zhishun Wang, J. Y. Cheung, Y. S. Xia, J. D.Z. Chen

Research output: Contribution to conferencePaperpeer-review

Abstract

Least absolute deviation (LAD) optimization model, or called unconstrained minimum L1-norm optimization model, has found extensive applications in linear parameter estimations. In this paper, neural implementation of LAD optimization model is presented, where a new neural network is constructed and its performance in performing LAD optimization is evaluated theoretically and experimentally. Then, the application of the proposed LAD network to time delay estimation (TDE) is presented. Compared with the popular time-delay estimation methods based on higher order spectra (HOS), our method is free of the assumption that the signal is non-Gaussian and the noises are Gaussian, which is closer to real situations.

Original languageEnglish (US)
Pages13-18
Number of pages6
StatePublished - Dec 1 1999
Externally publishedYes
EventProceedings of the 1999 Artificial Neural Networks in Engineering Conference (ANNIE '99) - St. Louis, MO, USA
Duration: Nov 7 1999Nov 10 1999

Other

OtherProceedings of the 1999 Artificial Neural Networks in Engineering Conference (ANNIE '99)
CitySt. Louis, MO, USA
Period11/7/9911/10/99

ASJC Scopus subject areas

  • Software

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