Using neural networks for fault diagnosis
IJCNN (5)（2000）
摘要
A universal fault instance model, which aims to solve problems existing in the present technology of fault diagnosis, such as the lack of universality, the difficulty in the use of real time systems and the dilemma of stability and plasticity, is proposed. An experiment demonstrates that the FANNC used can successfully settle the problems mentioned above by its effective incremental ability and processing new input patterns via one round learning
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关键词
fast adaptive neural network classifier,neural networks,real time systems,present technology,real time system,pattern classification,round learning,fannc,incremental ability,universality,plasticity,fault diagnosis,one round learning,universal fault instance model,new input pattern,stability,neural nets,fault detection,pattern analysis,neural network,artificial neural networks,automatic control,helium
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