Backpropagation architecture optimization and an application in nuclear power plant diagnostics
This paper presents a Dynamic Node Architecture (DNA) scheme to optimize the architecture of backpropagation Artificial Neural Networks (ANNs). This network scheme is used to develop an ANN based diagnostic adviser capable of identifying the operating status of a nuclear power plant. Specifically, a ``root`` network is trained to diagnose if the plant is in a normal operating condition or not. In the event of an abnormal condition, and other ``classifier`` network is trained to recognize the particular transient taking place. these networks are trained using plant instrumentation data gathered during simulations of the various transients and normal operating conditions at the Iowa Electric Light and Power Company`s Duane Arnold Energy Center (DAEC) operator training simulator.
- Research Organization:
- Iowa State Univ. of Science and Technology, Ames, IA (United States). Dept. of Mechanical Engineering
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- FG02-92ER75700
- OSTI ID:
- 10140175
- Report Number(s):
- CONF-930433-8; ON: DE93010308
- Resource Relation:
- Conference: 55. annual American power conference,Chicago, IL (United States),13-15 Apr 1993; Other Information: PBD: [1993]
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
NUCLEAR POWER PLANTS
REACTOR INSTRUMENTATION
REACTOR CONTROL SYSTEMS
NEURAL NETWORKS
TRANSIENTS
COMPUTERIZED SIMULATION
REACTOR OPERATORS
COMPUTER ARCHITECTURE
220400
990200
CONTROL SYSTEMS
MATHEMATICS AND COMPUTERS