Signal denoising in engineering problems through the minimum gradient method

D. A G Vieira, L. Travassos, R. R. Saldanha, Vasile Palade

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

This paper applies the minimum gradient method (MGM) to denoise signals in engineering problems. The MGM is a novel technique based on the complexity control, which defines the learning as a bi-objective problem in such a way to find the best trade-off between the empirical risk and the machine complexity. A neural network trained with this method can be used to pre-process data aiming at increasing the signal-to-noise ratio (SNR). After training, the neural network behaves as an adaptive filter which minimizes the cross-validation error. By applying the general singular value decomposition (GSVD), we show the relation between the proposed approach and the Wiener filter. Some results are presented, including a toy example and two complex engineering problems, which prove the effectiveness of the proposed approach.

Original languageEnglish
Pages (from-to)2270-2275
Number of pages6
JournalNeurocomputing
Volume72
Issue number10-12
DOIs
Publication statusPublished - Jun 2009
Externally publishedYes

Keywords

  • Inverse scattering
  • Multiobjective learning
  • Regression problems
  • Regularization methods
  • Wiener filter

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Cognitive Neuroscience

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