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Proceedings Paper

A Symbolic Neural Net Production System: Obstacle Avoidance, Navigation, Shift-Invariance And Multiple Objects
Author(s): David Casasent; Elizabeth Botha
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Paper Abstract

A symbolic neural net is described. It uses a multichannel symbolic correlator to produce input neuron data to an optical neural net production system. It has use in obstacle avoidance, navigation, and scene analysis applications. The shift-invariance and ability to handle multiple objects are novel aspects of this symbolic neural net. Initial simulated data are provided and symbolic optical filter banks are discussed. The neural net production system is described. A parallel and iterative set of rules and results for our case study are presented. Its adaptive learning aspects are noted.

Paper Details

Date Published: 1 March 1990
PDF: 11 pages
Proc. SPIE 1195, Mobile Robots IV, (1 March 1990); doi: 10.1117/12.969890
Show Author Affiliations
David Casasent, Carnegie Mellon University (United States)
Elizabeth Botha, Carnegie Mellon University (United States)


Published in SPIE Proceedings Vol. 1195:
Mobile Robots IV
Wendell H. Chun; William J. Wolfe, Editor(s)

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