Self-training artificial neural networks for risk reduction in nuclear power operations
Jouse, Wayne Curtis
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https://hdl.handle.net/2142/19192
Description
Title
Self-training artificial neural networks for risk reduction in nuclear power operations
Author(s)
Jouse, Wayne Curtis
Issue Date
1992
Doctoral Committee Chair(s)
Williams, J.G.
Department of Study
Nuclear, Plasma, and Radiological
Discipline
Nuclear Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Engineering, Electronics and Electrical
Engineering, Nuclear
Artificial Intelligence
Language
eng
Abstract
The risk reduction potential of the class of artificial neural networks based on the Barto-Sutton architecture is established. The risk associated with nuclear power operations is characterized by sequences of discrete events, such as technical specification violation. The Barto-Sutton architecture has the capability to synthesize precursors to these events, and to synthesize mitigative control policies. To establish the risk reduction potential of the network, network control of a complex reactor control task was demonstrated. The task exemplifies the structure of risk in modern nuclear power plant operation.
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