A neural net capable of restoring continuous level library vectors from memory is considered. The vectors in the memory library are used to program the neural interconnects. Given a portion of one of the library vectors, the net extrapolates the remainder. Sufficient conditions for unique convergence are stated. An architecture for optical implementation of the network is proposed.
Robert J. Marks,
Les E. Atlas,
Kwan F. Cheung,
"A Class of Continuous Level Neural Nets", Proc. SPIE 0813, Optics and the Information Age, (1 January 1987); doi: 10.1117/12.967138; https://doi.org/10.1117/12.967138