A Symbolic Neural Net Production System: Obstacle Avoidance, Navigation, Shift-Invariance And Multiple Objects
David Casasent, Elizabeth Botha · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1990
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.