The application of improved BP neural network in the engine fault diagnosis

BP neural network is the core part of the feedforward network, and embodies the core and the essence of the parts of the artificial neural network. The good nonlinear mapping ability of BP neural network can be a good application in fault diagnosis. But the traditional BP network has the trend of forgetting old samples during the training process when learning new samples, and exists the defect of low training accuracy. A neural network algorithm of increased state feedback in the output layer is designed in this paper to solve the problem above. The improved BP algorithm is used in the fault diagnosis of automotive engine, the indexes of the automobile exhaust are used as the inputs of the neural network, the outputs corresponding to the different misfire. The simulation results show the proposed algorithm can effectively improve the BP neural network training accuracy, and more accurately to achieve misfire diagnosis.