{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/19192"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/19192","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Self-training artificial neural networks for risk reduction in nuclear power operations","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. 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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.","Made available in DSpace on 2011-05-07T11:59:43Z (GMT). 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