Research On Failure Time Prediction Of Avionics System Based On Recurrent Neural Network

With the increasingly complex and highly integrated avionics systems of aircraft,there is a need for higher reliability and safety of the avionics systems,and the failure rate time series data prediction of the avionics system is one of the key ones Layer protection.Aiming at the problems of avionics system equipment failure rate prediction accuracy and prediction model parameter optimization digitization,this paper proposes an avionics system equipment failure rate prediction model based on particle swarm optimization optimized long and short-term memory neural network,and compares it with typical predictions.The model prediction results are compared to verify the effectiveness of this method.First,analyze the time series data of the equipment failure rate of the avionics system,and find out the characteristics of the data by contacting the cause of the equipment failure,and looking for a suitable prediction method based on the characteristics of a long data time span and a large correlation between the front and back.By analyzing and summarizing the scientific research directions of domestic and foreign experts and scholars,one of the variants of the cyclic neural network is selected.The long-and short-term memory neural network is characterized by solving long-term and closely related data.However,long-and short-term memory neural networks also have shortcomings.The key influencing parameters of the network model it establishes is highly liberalized and highly dependent on the experience of the experimenter.Therefore,the particle swarm algorithm is selected to perform global optimization of the bar parameters,and the original According to experience,the process of artificially setting parameters is changed to particle swarm algorithm to automatically iteratively search for optimal parameters.At the same time,the loss value of the key parameter iteration process is visualized.At the same time,the parameter inertia factor of particle swarm algorithm is optimized to strengthen the global optimization process of particle swarm Speed and accuracy.While establishing the LSTM prediction model and the PSO-LSTM prediction model,the RNN prediction model and the GRU prediction model are established as the control group,and the same test set data is used for training to obtain the prediction results.The error evaluation of each model is obtained by comparing with the real value.The results prove The avionics system equipment failure rate prediction model optimized by the particle swarm optimization algorithm has a better performance.