Back-Propagation Network for Analog Signal Separation in High Noise Environments.

Abstract

A back-propagation network is compared with principal components regression and prefiltered linear regression to demonstrate its ability to separate overlapped analog signals in high noise environments. Specifically, the signals tested were synthetically generated chemical mixture spectra that simulate the type of data obtained from chromatography and photo-spectrometry. The individual spectrum are heavily overlapped and 30 percent random noise and a random dc has been added to them. The comparisons were made for data sets comprised of two, three, and four overlapping spectrum. Neural networks, Chromatography, Back-propagation.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1992
Accession Number
ADA254245

Entities

People

  • Alice M. Harper
  • Richard G. Vanderbeek

Organizations

  • Edgewood Chemical Biological Center

Tags

Communities of Interest

  • Weapons Technologies

DTIC Thesaurus Topics

  • Analog Signals
  • Analytical Chemistry
  • Background Noise
  • Chemical Weapons
  • Chromatography
  • Data Sets
  • Detection
  • Eigenvalues
  • Engineering
  • Environment
  • Filtration
  • Governments
  • Neural Networks
  • Noise
  • Spectra
  • Test Sets
  • Transfer Functions

Readers

  • Approximation Theory.
  • Neural Network Machine Learning.
  • Phased Array Antenna Design.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks