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Proceedings Paper

Target tracking using impulsive analog circuits
Author(s): John G. Elias
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Paper Abstract

The electronic architecture and silicon implementation of an artificial neuron which can be used to process and classify dynamic signals is described. The electrical circuit architecture is modeled after complex neurons in the vertebrate brain which have spatially extensive dendritic tree structures that support large numbers of synapses. The circuit is primarily analog and, as in the biological model system, is virtually immune to process variations and other factors which often plague more conventional circuits. The nonlinear circuit is sensitive to both temporal and spatial signal characteristics but does not make use of the conventional neural network concept of weights, and as such does not use multipliers, adders, look-up-tables, microprocessors, or other complex computational devices. We show that artificial neural networks with passive dendritic tree structures can be trained, using a specialized genetic algorithm, to produce control signals useful for target tracking and other dynamic signal processing applications.

Paper Details

Date Published: 16 September 1992
PDF: 13 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.140012
Show Author Affiliations
John G. Elias, Univ. of Delaware (United States)


Published in SPIE Proceedings Vol. 1709:
Applications of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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