Applying neural networks to optimize instrumentation performance

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Description

Well calibrated instrumentation is essential in providing meaningful information about the status of a plant. Signals from plant instrumentation frequently have inherent non-linearities, may be affected by environmental conditions and can therefore cause calibration difficulties for the people who maintain them. Two neural network approaches are described in this paper for improving the accuracy of a non-linear, temperature sensitive level probe ised in Expermental Breeder Reactor II (EBR-II) that was difficult to calibrate.

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7 p.

Creation Information

Start, S.E. & Peters, G.G. June 1, 1995.

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This article is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. More information about this article can be viewed below.

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  • Argonne National Laboratory
    Publisher Info: Argonne National Lab., Idaho Falls, ID (United States)
    Place of Publication: Idaho Falls, Idaho

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Description

Well calibrated instrumentation is essential in providing meaningful information about the status of a plant. Signals from plant instrumentation frequently have inherent non-linearities, may be affected by environmental conditions and can therefore cause calibration difficulties for the people who maintain them. Two neural network approaches are described in this paper for improving the accuracy of a non-linear, temperature sensitive level probe ised in Expermental Breeder Reactor II (EBR-II) that was difficult to calibrate.

Physical Description

7 p.

Notes

INIS; OSTI as DE95009466

Source

  • 9. symposium on power plant dynamics, control and testing, Knoxville, TN (United States), 24-26 May 1995

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  • Other: DE95009466
  • Report No.: ANL/IFR/CP--84089
  • Report No.: CONF-950564--8
  • Grant Number: W-31-109-ENG-38
  • Office of Scientific & Technical Information Report Number: 79135
  • Archival Resource Key: ark:/67531/metadc742559

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Creation Date

  • June 1, 1995

Added to The UNT Digital Library

  • Oct. 19, 2015, 7:39 p.m.

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  • Jan. 6, 2016, 4:52 p.m.

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Start, S.E. & Peters, G.G. Applying neural networks to optimize instrumentation performance, article, June 1, 1995; Idaho Falls, Idaho. (digital.library.unt.edu/ark:/67531/metadc742559/: accessed August 16, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.