A Symbolic Neural Net Production System: Obstacle Avoidance, Navigation, Shift-Invariance And Multiple Objects

David Casasent; Elizabeth Botha

doi:10.1117/12.969890

1 March 1990 A Symbolic Neural Net Production System: Obstacle Avoidance, Navigation, Shift-Invariance And Multiple Objects

David Casasent, Elizabeth Botha

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Proceedings Volume 1195, Mobile Robots IV; (1990) https://doi.org/10.1117/12.969890
Event: 1989 Symposium on Visual Communications, Image Processing, and Intelligent Robotics Systems, 1989, Philadelphia, PA, United States

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.

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David Casasent and Elizabeth Botha "A Symbolic Neural Net Production System: Obstacle Avoidance, Navigation, Shift-Invariance And Multiple Objects", Proc. SPIE 1195, Mobile Robots IV, (1 March 1990); https://doi.org/10.1117/12.969890

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