Improved Deep Belief Network of Fault Diagnosis Integrated Navigation Systems
In order to improve the accuracy and stability of INS/GNSS integrated navigation system,a method based on improved deep belief network was proposed.Method based on state chi-square test(SCST)was used for real-time detection of integrated navigation systems,and the detection results used as sample data to improve deep belief network(DBN)training.The deep belief network was used to extract deep features and fault classi-fication from the data.Introducing radial basis functions(RBF)as the activation function of the model to im-prove the adaptability of deep belief networks to complex data distributions;using adaptive moment estimation(ADAM)algorithm instead of traditional gradient descent algorithm to improve the accuracy of fault diagnosis.The numerical simulation results showed that the accuracy of the algorithm in fault identification reaches 97%,which could effectively diagnose the fault types of the INS/GNSS integrated navigation system and ensured the smooth operation of the system.
integrated navigation systemfault diagnosisdeep belief networkradial basis functionadaptive moment estimation
万方数据
维普
