Self-training artificial neural networks for risk reduction in nuclear power operations

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Nuclear Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2011

Author dc:creator

• Jouse, Wayne Curtis

Contributors dc:contributor

• Williams, J.G.

• Engineering, Electronics and Electrical
• Engineering, Nuclear
• Artificial Intelligence

Statement dc:rights

• Copyright 1992 Jouse, Wayne Curtis

Language dc:language

eng

Identifier

AAI9215832
(UMI)AAI9215832

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/19192