Title :
Research on Military Equipment Fault Diagnosis Based on ANN and ES
Author_Institution :
Automobile Manage. Inst. of PLA, Bengbu, China
Abstract :
There are some shortages of knowledge acquisition and inefficency in ES. So, combines ES with ANN to construst military equipment fault diagosis expert system. Introduces the neural network learning system, the knowledge base and the reasoning mechanism of the expert system. After introducing ANN and ES, utilizing the adapting, self-learning abilities of ANN, methods of knowledge acquirement and representation are studied, ways of solving the bottleneck problem of knowledge acquirement in Intelligence Fault Diagnosis Expert system (IFDES) are discussed, and knowledge base of ES founded on ANN is put forward. In the end, the feasibility and validity is testified by an instance.
Keywords :
expert systems; fault diagnosis; knowledge acquisition; military equipment; neural nets; unsupervised learning; ANN; expert system; intelligence fault diagnosis expert system; knowledge acquisition; knowledge base; military equipment fault diagosis; neural network; reasoning mechanism; self learning; Artificial neural networks; Expert systems; Fault diagnosis; Ignition; Knowledge engineering; Training; expert system; fanlt diagnosis; military equipment; neural network;
Conference_Titel :
Intelligent Computation Technology and Automation (ICICTA), 2011 International Conference on
Conference_Location :
Shenzhen, Guangdong
Print_ISBN :
978-1-61284-289-9
DOI :
10.1109/ICICTA.2011.182