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

Wesley E. Foor; Mark Allen Neifeld

doi:10.1117/12.179121

29 June 1994 Adaptive optical radial basis function neural network for handwritten digit recognition

Wesley E. Foor, Mark Allen Neifeld

Proceedings Volume 2240, Advances in Optical Information Processing VI; (1994) https://doi.org/10.1117/12.179121
Event: SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, 1994, Orlando, FL, United States

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.

Citation Download Citation

Wesley E. Foor and Mark Allen Neifeld "Adaptive optical radial basis function neural network for handwritten digit recognition", Proc. SPIE 2240, Advances in Optical Information Processing VI, (29 June 1994); https://doi.org/10.1117/12.179121

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KEYWORDS

Neural networks

Computing systems

Computer simulations

Spatial light modulators

Adaptive optics

Data modeling

Optical networks

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