A neural network solver for basis pursuit and its applications to time-frequency analysis of biomedical signals

Z. S. Wang, Y. S. Xia, W. H. Li, Z. Y. He, J. D.Z. Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper the authors present a new neural network model, called the constrained smallest l1-norm neural network (CSl1NN), for basis pursuit (BP) implementation. The BP is considered as a large-scale linear programming problem. In contrast with the simplex-BP or inferior-BP, the proposed CSl1NN-BP does not double the optimizing scale and can be implemented in real time via hardware. Using non-stationary artificial signals and electrogastrograms to test our simulations show that the CSl 1NN-BP presents an excellent convergence performance for a wide range of time-frequency (TF) dictionaries and has a higher joint TF resolution not only than the traditional Wigner distribution, but also other overcomplete representation methods. Combining the high resolution with the fast implementation, the CSl1NN-BP can be used for online time-frequency analysis of various kinds of non-stationary signals including medical data, such as ECG, EEG and EGG.

Original languageEnglish (US)
Title of host publication1997 IEEE International Conference on Neural Networks, ICNN 1997
Pages2057-2060
Number of pages4
DOIs
StatePublished - Dec 1 1997
Externally publishedYes
Event1997 IEEE International Conference on Neural Networks, ICNN 1997 - Houston, TX, United States
Duration: Jun 9 1997Jun 12 1997

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
Volume4
ISSN (Print)1098-7576

Conference

Conference1997 IEEE International Conference on Neural Networks, ICNN 1997
Country/TerritoryUnited States
CityHouston, TX
Period6/9/976/12/97

ASJC Scopus subject areas

  • Software

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