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Proceedings Paper

Adaptive-optical radial-basis-function neural network for handwritten digit recognition
Author(s): Wesley E. Foor; Mark Allen Neifeld
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Paper Abstract

An adaptive optical radial basis function classifier for handwritten digit recognition 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 and 198 stored reference patterns in parallel using dual vector-matrix multipliers. For this experimental software is used to perform the on-line learning of the weights and basis function widths. An experimental recognition rate of 86.7% correct out of 300 testing samples is achieved with the adaptive training versus 52.3% correct for non-adaptive training. The experimental results from the optical system are compared with data from a computer model of the system in order to identify noise sources and indicate possible improvements for system performance.

Paper Details

Date Published: 29 June 1994
PDF: 9 pages
Proc. SPIE 2240, Advances in Optical Information Processing VI, (29 June 1994); doi: 10.1117/12.179121
Show Author Affiliations
Wesley E. Foor, Univ. of Arizona and Rome Lab. (United States)
Mark Allen Neifeld, Univ. of Arizona (United States)


Published in SPIE Proceedings Vol. 2240:
Advances in Optical Information Processing VI
Dennis R. Pape, Editor(s)

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