Hybrid expert system - neural network - Fuzzy Logic methodology for transient identification
- Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering
- Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering Oak Ridge National Lab., TN (United States)
A methodology is presented that demonstrates the potential of pretrained artificial neural networks (ANN's) as generators of membership functions for the purpose of transient identification in Nuclear Power Plants (NPP). In order to provide timely concise and task-specific information about the many aspects of the transient and to determine the state of the system based on the interpretation of potentially noisy data, a model-referenced approach is utilized, where pretrained ANNs provide the model. Membership functions -- that condense information about a transient in a form convenient for a rule-based identification system -- are produced through ANN'S. The results demonstrate the extremely good noise-tolerance of ANN's and suggest a new method for transient identification within the framework of Fuzzy Logic.
- Research Organization:
- Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering
- Sponsoring Organization:
- DOE; USDOE, Washington, DC (United States)
- DOE Contract Number:
- FG07-88ER12824
- OSTI ID:
- 6862312
- Report Number(s):
- CONF-9109447-2; ON: DE93003567
- Country of Publication:
- United States
- Language:
- English
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Fuzzy logic -- artificial neural networks integration for transient identification
Fuzzy logic -- artificial neural networks integration for transient identification
Related Subjects
220900* -- Nuclear Reactor Technology-- Reactor Safety
99 GENERAL AND MISCELLANEOUS
990200 -- Mathematics & Computers
ACCIDENTS
EXPERT SYSTEMS
FUZZY LOGIC
MATHEMATICAL LOGIC
NEURAL NETWORKS
NUCLEAR FACILITIES
NUCLEAR POWER PLANTS
POWER PLANTS
REACTOR ACCIDENTS
THERMAL POWER PLANTS
TRANSIENTS