Generation of knowledge base for Space Acceleration Measurement System (SAMS) data using an adaptive resonance theory 2-A (ART2-A) neural network

Andrew D. Smith; Alok Sinha

doi:10.1117/12.304837

25 March 1998 Generation of knowledge base for Space Acceleration Measurement System (SAMS) data using an adaptive resonance theory 2-A (ART2-A) neural network

Andrew D. Smith, Alok Sinha

Author Affiliations +

Proceedings Volume 3390, Applications and Science of Computational Intelligence; (1998) https://doi.org/10.1117/12.304837
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

Abstract

Events aboard the space shuttle such as crew movement, crew exercise, thruster firings, etc., disrupt the microgravity environment required for many on-board experiments. Automatic detection of these events would allow astronauts to minimize their impact on experiments. Hence, using Space Acceleration Measurement System (SAMS) data collected on the USMP-3 mission, a knowledge base is generated to aid in the detection of disruptive events aboard the USMP-4 mission. Input patterns containing power spectral density (PSD) information of SAMS data are used to train an Adaptive Resonance Theory 2-A (ART2- A) neural network. The ART2-A neural network has been chosen because it has the ability to automatically add clusters as new input patterns are presented. The weight vectors of the ART2-A are used as the knowledge base. Using characteristic frequencies and acceleration magnitudes determined by Principal Investigator Microgravity Services (PIMS), each weight vector is assigned a label or name representing a set of events. The labeled knowledge base is then tested by presenting input patterns created from data collected during an exercise event.

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Andrew D. Smith and Alok Sinha "Generation of knowledge base for Space Acceleration Measurement System (SAMS) data using an adaptive resonance theory 2-A (ART2-A) neural network", Proc. SPIE 3390, Applications and Science of Computational Intelligence, (25 March 1998); https://doi.org/10.1117/12.304837

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KEYWORDS

Neural networks

Neurons

Binary data

Antennas

Environmental sensing

Fluctuations and noise

Ku band

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