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Optical Engineering

Fractional Fourier transforms, wavelet transforms, and adaptive neural networks
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Paper Abstract

A new optical architecture is developed, based on fractional Fourier transforms, that compromises between shift-invariant (frequency) and position-dependent filtering. The analogy of this architecture to wavelet transforms and adaptive neural networks is also presented. The ambiguity and Wigner distribution functions are obtainable from special cases of the filter. The filter design corresponds to the training of the neural networks, and an adaptive learning algorithm is developed based on gradient-descent error minimization and error back propagation. The extension to multilayer architecture is straightforward.

Paper Details

Date Published: 1 July 1994
PDF: 5 pages
Opt. Eng. 33(7) doi: 10.1117/12.172793
Published in: Optical Engineering Volume 33, Issue 7
Show Author Affiliations
Soo-Young Lee, Korea Advanced Institute of Science and Technology (South Korea)
Harold H. Szu, Naval Surface Warfare Ctr. (United States)

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