Abstract

[russ%yummy@gateway.mitre.org]

For copies of the following paper send to:
  Wieland at mitre.arpa
  or
  Alexis Wieland
  M.S. Z425
  MITRE Corporation
  7525 Colshire Drive
  McLean, Virginia 22102


                An Analysis of Noise Tolerance for a Neural Network 
                        	Recognition System

	 		          Alexis Wieland 
				 Russell Leighton
				   Garry Jacyna

			        MITRE Corporation
			     Signal Processing Center
			       7525 Colshire Drive
			      McLean, Virginia 22102

  This paper analyzes the performance of a neural network designed to carry
  out a simple recognition task when its input signal has been corrupted with
  gaussian or correlated noise. The back-propagation algorithm was used to
  train a neural network to categorize input images as being an A, B, C, D,
  or nothing independent of rotation, contrast, and brightness, and in the
  presence of large amounts of additive noise. For bandlimited white gaussian 
  noise the results are compared to the performance of an optimal matched
  filter. The neural network is shown to perform classification at or near 
  the optimal limit.