After compliance verification, operating vehicles can enter the road transportation market. Diesel engine is the main power source of these vehicles, there will be some typical faults during the use of diesel engine, ...
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After compliance verification, operating vehicles can enter the road transportation market. Diesel engine is the main power source of these vehicles, there will be some typical faults during the use of diesel engine, which will affect the technical status of vehicles. According to the fault diagnosis problem of diesel engine, a fault diagnosis method based on adaptive-network-based fuzzy inference system(ANFIS) was proposed, Subtractive clustering algorithm was used to confirm the original structure of fuzzyinference model, and ANFIS was used to build an original fault diagnosis model of diesel engine. Hybrid algorithm is used to train the parameter of fuzzy rule, and the final model is established. Simulation experiment results show that the modeling algorithm based on subtractive clustering-ANFIS is effective. It has been found that the average error is 7%, the recognition accuracy is 93.33%. Simulation results show that the fitting ability, convergence speed and recognition accuracy of ANFIS model are all superior to back propagation neural networks (BPNN), and much more suitable as diesel engine fault diagnosis model. Finally, an effective fault diagnosis system is developed by using the given method.
In this paper, a modified analysis of Four Wave Mixing (FWM) by incorporating the third-order dispersion parameters is reported. With the help of a modified formula for FWM crosstalk, the effect of second-and third-or...
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In this paper, a modified analysis of Four Wave Mixing (FWM) by incorporating the third-order dispersion parameters is reported. With the help of a modified formula for FWM crosstalk, the effect of second-and third-order dispersion parameters at different input channel powers has been analysed. Under the combined effect of second-and third-order dispersion parameters, the crosstalk introduced by FWM is reduced. Further, we propose a fuzzy-based approach using adaptive-network-based fuzzy inference system (ANFIS) to calculate FWM power by varying input channel powers at different intensity-dependent phase-matching factors, and observed that the results are very similar to analytical results.
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