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Title:Self-training artificial neural networks for risk reduction in nuclear power operations
Author(s):Jouse, Wayne Curtis
Doctoral Committee Chair(s):Williams, J.G.
Department / Program:Nuclear, Plasma, and Radiological
Discipline:Nuclear Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Engineering, Electronics and Electrical
Engineering, Nuclear
Artificial Intelligence
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.
Issue Date:1992
Type:Text
Language:English
URI:http://hdl.handle.net/2142/19192
Rights Information:Copyright 1992 Jouse, Wayne Curtis
Date Available in IDEALS:2011-05-07
Identifier in Online Catalog:AAI9215832
OCLC Identifier:(UMI)AAI9215832


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