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oai:www.ideals.illinois.edu:2142/19192 2023-07-10 col_2142_5131 col_2142_14837 com_2142_5130 com_2142_14836 com_2142_234

Williams, J.G. Jouse, Wayne Curtis 2011-05-07T11:59:43Z 2011-05-07T11:59:43Z 10000-01-01 1992 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. Made available in DSpace on 2011-05-07T11:59:43Z (GMT). No. of bitstreams: 2 license.txt: 4922 bytes, checksum: 910b249b4beec47e7ab768910c8f966f (MD5) 9215832.pdf: 3672427 bytes, checksum: 14367f4961d56d21537f5fa5c0629f45 (MD5) Previous issue date: 1992 Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding (hding2@illinois.edu) on 2011-05-07T14:35:16Z Item is restricted indefinitely. Restriction data tranferred 2014-07-01T11:13:51-05:00 Original Data Group with Access UIUC Users [automated] Release Date: none Reason: ETDs are only available to UIUC Users without author permission ETDs are only available to UIUC Users without author permission U of I Only AAI9215832 (UMI)AAI9215832 http://hdl.handle.net/2142/19192 eng Copyright 1992 Jouse, Wayne Curtis Engineering, Electronics and Electrical Engineering, Nuclear Artificial Intelligence Self-training artificial neural networks for risk reduction in nuclear power operations text Nuclear, Plasma, and Radiological Nuclear Engineering University of Illinois at Urbana-Champaign Dissertation Ph.D.