Application Research of RBF Neural Network Based on Improved Bat Algorithm in Fault Diagnosis of Train-Ground Wireless Communication Equipment

Considering the ambiguity, complexity and uncertainty of the train-ground wireless communication equipment, to locate the cause of the fault in time and improve the diagnostic accuracy, a fault diagnosis method for the train-ground wireless communication equipment with improved bat algorithm (IBA) optimized RBF neural network is proposed. In order to improve the global search ability of the bat algorithm, based on the standard bat algorithm, the fuzzy RBF neural network structural parameters are optimized by improving the inertia weight of the bat algorithm and then performing the Gaussian mutation operation on the bat algorithm. Therefore, the fault diagnosis model of train-ground wireless communication equipment based on IBA optimized fuzzy RBF neural network is established. The simulation results show that compared with the standard BA-optimized fuzzy neural network, the accuracy of fault diagnosis of the IBA optimized fuzzy RBF neural network is greatly improved, and the number of trainings is reduced to 29 steps.