DocumentCode
2845788
Title
The Research of Fault Diagnosis for Gasoline Engine Based on RS-ANN
Author
Tian Li ; Li, Tian
Author_Institution
Anhui Univ. of Technol. & Sci., Wuhu, China
fYear
2009
fDate
19-20 Dec. 2009
Firstpage
1
Lastpage
4
Abstract
Considering the reduction ability of rough set theory and the classification ability of fuzzy neural network, a rough set-neural network combinatorial fault-diagnosing model is constructed. The model enjoys a better topological structure and greatly increased speed for learning. The practical application to fault diagnosis for gasoline engine verifies that the model has comparably fast and accurate diagnosing abilities.
Keywords
fault diagnosis; fuzzy neural nets; internal combustion engines; mechanical engineering computing; rough set theory; RS-ANN; fuzzy neural network; gasoline engine; rough set theory; rough set-neural network combinatorial fault-diagnosing model; Artificial neural networks; Data security; Decision making; Engines; Fault diagnosis; Fuzzy neural networks; Information systems; Mathematical model; Petroleum; Set theory;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Engineering and Computer Science, 2009. ICIECS 2009. International Conference on
Conference_Location
Wuhan
Print_ISBN
978-1-4244-4994-1
Type
conf
DOI
10.1109/ICIECS.2009.5365071
Filename
5365071
Link To Document