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- W2391093290 abstract "In order to solve the fault diagnosis problem of diesel engine,Adaptive Neuro-Fuzzy inference system(ANFIS) was applied to build a fault diagnosis model of diesel engine.Subtractive clustering algorithm was used to confirm the original structure of fuzzy inference model,and a hybrid algorithm made up of the least-squares method and the backpropagation gradient descent method was adopted to optimize the model parameters.Through verification of the built diagnosis model with data of engine tests,it has been found that the maximal error between the identification and actual values of fault is 10.16%,the minimal error and the average error is respectively 0.115% and 2.26%,the recognition accuracy is 97.74%.Simulation results show that the fitting ability,convergence speed and recognition accuracy of ANFIS model are all superior to back propagation neural networks(BPNN).So a contingent fault of diesel engine can be identified effectively." @default.
- W2391093290 created "2016-06-24" @default.
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- W2391093290 date "2008-01-01" @default.
- W2391093290 modified "2023-09-23" @default.
- W2391093290 title "Fault Diagnosis of Diesel Engine Based on ANFIS" @default.
- W2391093290 hasPublicationYear "2008" @default.
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