Hybrid expert system - neural network - Fuzzy Logic methodology for transient identification
- Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering
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:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- FG07-88ER12824
- OSTI ID:
- 10107971
- Report Number(s):
- CONF-9109447-2; ON: DE93003567
- Resource Relation:
- Conference: 2. government neural network applications workshop,Huntsville, AL (United States),10-12 Sep 1991; Other Information: PBD: [1991]
- Country of Publication:
- United States
- Language:
- English
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A hybrid neural network---fuzzy logic approach to nuclear power plant transient identification
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