A neural network solver for Basis Pursuit

Zhishun Wang, Zhenya He, Youshen Xia, J. D.Z. Chen

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, the authors present a new neural network model, which can be called constrained smallest l1-norm neural network (CSl1NN), to implement the Basis Pursuit (BP)[1-3]. As the new and generalised one of the communities of over-complete signal representations, the BP is considered as a larger-scale linear programming problem. In contrast with the Simplex-BP or interior-BP in [2], the proposed CSl1NN-BP does not double the optimizing scale and can be implemented in real time through hardware. Taking non-static nary artificial signals to test, our simulations show that the CSl1NN 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 (WD), but also than recently rising other overcomplete representation methods, such as Method of Frames (MOF)[9], Best Orthogonal Basis (BOB)[10] and Matching Pursuit (MP)[4]. Combining the high resolution with the fast implementation, the CSl1 NN-BP will be very promising for on-line time-frequency analysis of various kinds of non-stationary signals with high quality.

Original languageEnglish (US)
Pages (from-to)86-92
Number of pages7
JournalChinese Journal of Electronics
Volume7
Issue number1
StatePublished - Jan 1 1998
Externally publishedYes

Keywords

  • Basis pursuit (BP)
  • Constrained smallest l-norm neural network (CSl- NN)

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Applied Mathematics

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