Fuzzy logic -- artificial neural networks integration 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 integrates pretrained artificial neural networks (ANNs) with rule-based fuzzy logic systems, for the purpose of distinguishing different transients in a Nuclear Power Plant (NPP). In general this approach appears to provide timely, concise and task specific information about the status of a system under consideration. The pretrained neural network typifies different transient scenarios and derives membership functions which independently represent individual transients. The overall system successfully performs transient identification, in a time span faster or at least comparable to that of transient development. In order to examine the proposed methodology simulated accidents are used. The results obtained demonstrate the excellent noise tolerance of ANNs and suggest a new approach for transient identification.
- 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:
- 7188595
- Report Number(s):
- CONF-9111215-1; ON: DE93002232
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
220400* -- Nuclear Reactor Technology-- Control Systems
99 GENERAL AND MISCELLANEOUS
990200 -- Mathematics & Computers
ARTIFICIAL INTELLIGENCE
FUZZY LOGIC
MATHEMATICAL LOGIC
NEURAL NETWORKS
NUCLEAR FACILITIES
NUCLEAR POWER PLANTS
POWER PLANTS
TESTING
THERMAL POWER PLANTS
TRANSIENTS