Gradient genetic algorithm-based oil fault diagnosis model for automobile engine

Adaptive neural network-based fuzzy inference system (ANFIS) was applied to build a fault diagnosis model of automobile engine, the thesis, 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, the ANFIS fault diagnosis model adopts the method of information fusion in entropy method to optimize the input interface. The gradient genetic algorithm is adopted to optimize the internet parameters, so as to speed up learning. Through verification of the build diagnosis model with data of engine tests, it has been found that the recognition accuracy attain to 98.99%, training error falling to 0.02038. The experiment indicates that gradient descent genetic algorithm is a fast algorithm that can support the local optimization of individual chromosome and the global optimization of chromosomes in a group.