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Proceedings Paper

Neural Analog Processing
Author(s): Robert Hecht-Nielsen
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Paper Abstract

This paper presents a bionic approach to pattern classification entitled Neural Analog Processing (NAP). NAP systems are based upon information processing principles discovered by neural modelers, but are not themselves neural models. To set the stage for a discussion of how NAP systems work, the theory of a particular type of local-in-time template-matching classifier -- the Generalized Nearest Neighbor (GNN) classifier -- for general time-varying patterns (imagery, spectra, tactile signals, etc.) is reviewed. The definition and function of the fundamental NAP structure -- the slab -- is then presented and it is shown that a GNN classifier can, in principle, be implemented using slabs. The embellishments necessary to allow NAP systems to be realized in hardware are then described. Finally, a summary of NAP system characteristics is presented.

Paper Details

Date Published: 23 May 1983
PDF: 10 pages
Proc. SPIE 0360, Robotics and Industrial Inspection, (23 May 1983); doi: 10.1117/12.934100
Show Author Affiliations
Robert Hecht-Nielsen, Motorola Government Electronics Group (United States)


Published in SPIE Proceedings Vol. 0360:
Robotics and Industrial Inspection
David P. Casasent, Editor(s)

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