Neural networks applied to diffraction-pattern sampling.

While diffraction-pattern sampling has been widely applied in the classification of patterns, still its usage has been limited somewhat by the need to devise rather sophisticated algorithms. In this paper we describe sorting or classification of a variety of patterns with commercially available neural-network software together with the ring-wedge photodetector to supply optical transform data for the input neurons. With this combination of neural networks and diffraction-pattern sampling it is no longer necessary to write specialized software. The training and testing methodology is carried out for this new system, and excellent results are obtained for sorting thumbprints. In sorting thumbprints the neural network can be trained for orientation-independent or wide-scale size-independent classifications by use of ring-only or wedge-only input neurons, respectively. Separate experiments are described for the sorting of particulates. Again, these are cases in which writing appropriate software based on diffraction theory would be extremely difficult. Two interesting novel neural networks are obtained: one is for real-time control of a submicrometer colloidal suspension of CdS, and the second is for concentration measurements of 2.02-µm polyvinyltoulene spheres in methyl alcohol. Widespread new applications are predicted for this hybrid system that combines diffraction-pattern sampling and the neural network.