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Proceedings Paper

Neural network-based sensor signal accelerator
Author(s): Michael C. Vogt
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Paper Abstract

A strategy has been developed to computationally accelerate the response time of a generic electronic sensor. The strategy can be deployed as an algorithm in a control system or as a physical interface (on an embedded microcontroller) between a slower responding external sensor and a higher- speed control system. Optional code implementations are available to adjust algorithm performance when computational capability is limited. In one option, the actual sensor signal can be sampled at the slower rate with adaptive linear neural networks predicting the sensor's future output and interpolating intermediate synthetic output values. In another option, a synchronized collection of predictors sequentially controls the corresponding synthetic output voltage. Error is adaptively corrected in both options. The core strategy has been demonstrated with automotive oxygen sensor data. A prototype interface device is under construction. The response speed increase afforded by this strategy could greatly offset the cost of developing a replacement sensor with a faster physical response time.

Paper Details

Date Published: 2 February 2001
PDF: 8 pages
Proc. SPIE 4191, Sensors and Controls for Intelligent Manufacturing, (2 February 2001); doi: 10.1117/12.417242
Show Author Affiliations
Michael C. Vogt, Argonne National Lab. (United States)

Published in SPIE Proceedings Vol. 4191:
Sensors and Controls for Intelligent Manufacturing
Peter E. Orban; George K. Knopf, Editor(s)

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