Accession Number:

ADA297003

Title:

Adaptive Optical Radial Basis Function Neural Network Classifier.

Descriptive Note:

Rept. for Oct 93-Dec 94,

Corporate Author:

ROME LAB GRIFFISS AFB NY

Personal Author(s):

Report Date:

1994-12-01

Pagination or Media Count:

42.0

Abstract:

An adaptive optical radial basis function neural network classifier is experimentally demonstrated. We describe a spatially multiplexed system incorporating on-line adaptation of weights and basis function widths to provide robustness to optical system imperfections and system noise. The optical system computes the Euclidean distances between a 100-dimensional input vector and 198 stored reference patterns in parallel using dual vector-matrix multipliers and a contrast-reversing spatial light modulator. Software is used to emulate an analog electronic chip that performs the on-line learning of the weights and basis function widths. An experimental recognition rate of 92.7 correct out of 300 testing samples is achieved with the adaptive training versus 31.0 correct for non-adaptive training. We compare the experimental results with a detailed computer model of the system in order to analyze the influence of various noise sources on the system performance. KAR P. 3

Subject Categories:

  • Computer Hardware
  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE