Adaptive-optical radial-basis-function neural network for handwritten digit recognition

Wesley E. Foor, Mark A. Neifeld

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
PublisherPubl by Society of Photo-Optical Instrumentation Engineers
Number of pages9
ISBN (Print)081941544X
StatePublished - 1994
Externally publishedYes
EventAdvances in Optical Information Processing VI - Orlando, FL, USA
Duration: Apr 6 1994Apr 7 1994

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


OtherAdvances in Optical Information Processing VI
CityOrlando, FL, USA

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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