Target tracking using impulsive analog circuits

John G. Elias

doi:10.1117/12.140012

16 September 1992 Target tracking using impulsive analog circuits

John G. Elias

Proceedings Volume 1709, Applications of Artificial Neural Networks III; (1992) https://doi.org/10.1117/12.140012
Event: Aerospace Sensing, 1992, Orlando, FL, United States

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.

Citation Download Citation

John G. Elias "Target tracking using impulsive analog circuits", Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); https://doi.org/10.1117/12.140012

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