Applied research on fuzzy neural network technology of nuclear power station safety evaluation
- 1. Institute of Nuclear Thermal-Hydraulic Safety and Standardization, North China Electric Power Univ., Beijing (China)
Description
Fuzzy neural network technology is a newly kind of developed intelligent technology, which includes the advantages of neural network and vague theory. The vague neural network technology evaluation methodology is applied to safety assessment of advanced reactor using this advantage, and we program with the neural network and the traditional language to compare probability of a core melt, The results show that the vague neural network technology calculation results agree with traditional calculation results and the vague neural network technology does not only guarantee the safety of the nuclear power station but enhances the efficiency, so neural network technology is a further developed new way on safety assessment of nuclear power. (authors)
Additional details
Publishing Information
- Publisher
- Atomic Energy Press
- Imprint Place
- Beijing (China)
- ISBN
- 978-7-5022-5040-9
- Imprint Title
- Progress report on nuclear science and technology in China (Vol.1). Proceedings of academic annual meeting of China Nuclear Society in 2009, No.3--nuclear power sub-volume (Pt.2)
- Imprint Pagination
- 621 p.
- Journal Page Range
- p. 880-886
Conference
- Title
- academic annual meeting of China Nuclear Society
- Acronym
- '09
- Dates
- 18-20 Nov 2009
- Place
- Beijing (China)
INIS
- Country of Publication
- China
- Country of Input or Organization
- China
- INIS RN
- 44023301
- Subject category
- S21: SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS;
- Resource subtype / Literary indicator
- Conference
- Descriptors DEI
- COMPARATIVE EVALUATIONS; EFFICIENCY; FUZZY LOGIC; NEURAL NETWORKS; NUCLEAR POWER; NUCLEAR POWER PLANTS; PROBABILITY; REACTOR SAFETY; RISK ASSESSMENT
- Descriptors DEC
- EVALUATION; MATHEMATICAL LOGIC; NUCLEAR FACILITIES; POWER; POWER PLANTS; SAFETY; THERMAL POWER PLANTS
Optional Information
- Notes
- 3 figs., 3 tabs., 7 refs.