Signal denoising in engineering problems through the minimum gradient method

Neurocomputing

Volumn 72, Issue 10-12, 2009, Pages 2270-2275

  Vieira, D A G ^a   Travassos, L ^b   Saldanha, R R ^a   Palade, Vasile ^c  

^a FEDERAL UNIVERSITY OF MINAS GERAIS   (Brazil)

^b SENAI CIMATEC   (Brazil)

^c UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

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

Indexed keywords

INVERSE SCATTERING; MULTIOBJECTIVE LEARNING; REGRESSION PROBLEMS; REGULARIZATION METHODS; WIENER FILTER;

ACOUSTIC INTENSITY; ADAPTIVE FILTERING; ADAPTIVE FILTERS; DYNAMIC LOADS; EDUCATION; ELECTRIC FILTERS; ENGINEERING EXHIBITIONS; GRADIENT METHODS; MULTIOBJECTIVE OPTIMIZATION; NEURAL NETWORKS; SCATTERING; SIGNAL PROCESSING; SIGNAL TO NOISE RATIO; SINGULAR VALUE DECOMPOSITION;

INVERSE PROBLEMS;

ANALYTICAL ERROR; ARTICLE; ARTIFICIAL NEURAL NETWORK; EMPIRICISM; ENGINEERING; INFORMATION PROCESSING; LEARNING; MATHEMATICAL COMPUTING; NOISE REDUCTION; PRIORITY JOURNAL; SIGNAL NOISE RATIO; VALIDATION PROCESS;

EID: 67349112381     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2009.01.004     Document Type: Article

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