Virtual measurements using neural networks and fuzzy logic
- Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering
This paper reports on a fuzzy-neural methodology which is developed for the purpose of measuring plant parameters (e.g., performance) difficult to estimate in a timely and reliable manner. In the virtual measurement methodology presented the output of a measurement is considered to be a fuzzy number, called Virtual Measurement Value (VMV). VMVs are obtained through a complex-to-simple mapping performed by artificial neural networks. Each virtual measurement value is uniquely identified by a trapezoidal membership function learned by a simple network using the back-propagation learning algorithm. The actual output at any given time is selected through an index of dissemblance, which is a measure of similarity between two fuzzy numbers. The methodology is tested with start-up data form an experimental nuclear reactor.
- OSTI ID:
- 7116959
- Report Number(s):
- CONF-920432--
- Journal Information:
- Proceedings of the American Power Conference; (United States), Journal Name: Proceedings of the American Power Conference; (United States) Vol. 54:2; ISSN PAPWA; ISSN 0097-2126
- Country of Publication:
- United States
- Language:
- English
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220600* -- Nuclear Reactor Technology-- Research
Test & Experimental Reactors
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990200 -- Mathematics & Computers
ALGORITHMS
ANALOG SYSTEMS
COMPUTER-AIDED DESIGN
COMPUTERIZED CONTROL SYSTEMS
CONTROL SYSTEMS
DESIGN
EXPERIMENTAL REACTORS
FUNCTIONAL MODELS
FUZZY LOGIC
MATHEMATICAL LOGIC
NEURAL NETWORKS
NUCLEAR FACILITIES
NUCLEAR POWER PLANTS
ON-LINE CONTROL SYSTEMS
ON-LINE SYSTEMS
PARALLEL PROCESSING
PERFORMANCE TESTING
POWER PLANTS
PROGRAMMING
REACTOR COMPONENTS
REACTOR SIMULATORS
REACTORS
RELIABILITY
RESEARCH AND TEST REACTORS
SIMULATORS
TESTING
THERMAL POWER PLANTS