A Neural Architecture for Unsupervised Learning with Shift, Scale and Rotation Invariance, Efficient Software Simulation Heuristics, and Optoelectronic Implementation

Donald C. Wunsch, Missouri University of Science and Technology
D. S. Newman
T. P. Caudell
R. A. Falk
C. David Capps

This document has been relocated to http://scholarsmine.mst.edu/ele_comeng_facwork/1377

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A simple modification of the adaptive resonance theory (ART) neural network allows shift, scale and rotation invariant learning. The authors point out that this can be accomplished as a neural architecture by modifying the standard ART with hardwired interconnects that perform a Fourier-Mellin transform, and show how to modify the heuristics for efficient simulation of ART architectures to accomplish the additional innovation. Finally, they discuss the implementation of this in optoelectronic hardware, using a modification of the Van der Lugt optical correlator