Fuzzy controllers in nuclear material accounting
Fuzzy controllers are applied to predicting and modeling a time series, with particular emphasis on anomaly detection in nuclear material inventory differences. As compared to neural networks, the fuzzy controllers can operate in real time; their learning process does not require many iterations to converge. For this reason fuzzy controllers are potentially useful in time series forecasting, where the authors want to detect and identify trends in real time. They describe an object-oriented implementation of the algorithm advanced by Wang and Mendel. Numerical results are presented both for inventory data and time series corresponding to chaotic situations, such as encountered in the context of strange attractors. In the latter case, the effects of noise on the predictive power of the fuzzy controller are explored.
- Research Organization:
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- W-7405-ENG-36
- OSTI ID:
- 10186565
- Report Number(s):
- LA-UR-94-3116; CONF-9409112-1; ON: DE95000864; TRN: AHC29501%%139
- Resource Relation:
- Conference: FLINS-1: 1. international workshop on fuzzy logic and intelligent technologies in nuclear science,Mol (Belgium),14-16 Sep 1994; Other Information: PBD: [1994]
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
NUCLEAR MATERIALS MANAGEMENT
FUZZY LOGIC
TIME-SERIES ANALYSIS
MATERIAL UNACCOUNTED FOR
REAL TIME SYSTEMS
THEORETICAL DATA
SAFEGUARDS
STOCHASTIC PROCESSES
055001
990200
TECHNICAL ASPECTS
MATHEMATICS AND COMPUTERS