Abstract

We describe two optical systems based on the radial basis function approach to pattern classification. An optical-disk-based system for handwritten character recognition is demonstrated. The optical system computes the Euclidean distance between an unknown input and 650 stored patterns at a demonstrated rate of 26,000 pattern comparisons/s. The ultimate performance of this system is limited by optical-disk resolution to 10¹¹ binary operations/s. An adaptive system is also presented that facilitates on-line learning and provides additional robustness.

© 1993 Optical Society of America

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

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