ANFIS-based fault diagnosis cloud model of oil parameter for automobile engine

The thesis, in order to solve the fault diagnosis problem of oil Parameter, adaptive neural network-based fuzzy inference system (ANFIS) was applied to build a fault diagnosis model of automobile engine, with the construction of ANFIS, by using gradient descent genetic algorithm and optimization of system parameters of neutral network learning algorithm, inputs the fusion data into ANFIS, and introduces cloud model in output. Through verification of the build diagnosis model with data of engine tests, it has been found that the recognition accuracy increase from 90.26% to 98.72%, training error falling from 0.044237 to 0.02711. The experiment indicates that the recognition rate of "ANFIS + cloud model" system is significantly better than independent neural network reasoning system, fuzzy inference system and adaptive fuzzy neural network system.