@inproceedings{d97ad932a86448ada84d4915502eab80,
title = "Adaptive-optical radial-basis-function neural network for handwritten digit recognition",
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.",
author = "Foor, {Wesley E.} and Neifeld, {Mark A.}",
year = "1994",
doi = "10.1117/12.179121",
language = "English (US)",
isbn = "081941544X",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "Publ by Society of Photo-Optical Instrumentation Engineers",
pages = "155--163",
booktitle = "Proceedings of SPIE - The International Society for Optical Engineering",
note = "Advances in Optical Information Processing VI ; Conference date: 06-04-1994 Through 07-04-1994",
}