Published 1995 | Version v1
Miscellaneous

The fundamentals of fuzzy neural network and application in nuclear monitoring

  • 1. The Institute of Chemical Defence, Beijing (China)

Description

The authors presents a fuzzy modeling method using fuzzy neural network with the back-propagation algorithm. The new method can identify the fuzzy model of a nonlinear system automatically. Fuzzy neural network is used to generate fuzzy rules and membership functions. The feasibility and inferential statistic of the method is examined by using numerical data and XOR problem. The FNN improves accuracy and reliability, reduces design time and minimizes system cost of fuzzy design. The FNN can be used for estimation of human injury in nuclear explosions and can be simplified to a rule neural network (RNN), which is used for pole extraction of signal. Preliminary simulation show that FNN has vest vistas in nuclear monitoring

Part of:
Proceedings of the 8th national conference on computer application in science and technology

Additional details

Publishing Information

Imprint Title
Proceedings of the 8th national conference on computer application in science and technology
Imprint Pagination
340 p.
Journal Page Range
p. 211-215, 223.

Conference

Title
8. national conference on computer application in science and technology.
Dates
27 Oct - 1 Nov 1995.
Place
Huangshan (China).

INIS

Country of Publication
China
Country of Input or Organization
China
INIS RN
28005408
Subject category
S42: ENGINEERING; S99: GENERAL AND MISCELLANEOUS;
Resource subtype / Literary indicator
Conference, Non-conventional Literature
Descriptors DEI
ACCURACY; ALGORITHMS; FEASIBILITY STUDIES; FUZZY LOGIC; NEURAL NETWORKS; NUCLEAR EXPLOSION DETECTION; TESTING
Descriptors DEC
DETECTION; MATHEMATICAL LOGIC

Optional Information