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Proceedings Paper

Diagnostics and control of pressurized reactors using artificial neural networks
Author(s): Andreas Ikonomopoulos; Lefteri H. Tsoukalas; Robert E. Uhrig
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Paper Abstract

A methodology employing artificial neural networks and fuzzy arithmetic in the diagnosis and control of complex systems such as pressurized water reactors is presented. Fuzzy numbers represent the linguistic values of plant-specific variables, e.g., performance or availability. The notion of a virtual instrument, i.e., a software-based measuring device calibrated to the idiosyncrasies of a specific system is used. Neural networks perform a mapping of physically measurable parameters to fuzzy numbers called Virtual Measurement Values (VMV). The methodology is tested with start-up data from an experimental nuclear reactor. The results demonstrate the very good capacity of such virtual instruments for failure-tolerance and suggest the possibility of developing alternative algorithms for diagnostics and control.

Paper Details

Date Published: 16 September 1992
PDF: 8 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.139988
Show Author Affiliations
Andreas Ikonomopoulos, Univ. of Tennessee/Knoxville (United States)
Lefteri H. Tsoukalas, Univ. of Tennessee/Knoxville (United States)
Robert E. Uhrig, Univ. of Tennessee/Knoxville (United States)


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

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