Research on Fault Diagnosis of Neural Network Based on Bee Colony Algorithm Optimization in Gun Control System

Abstract

Aiming at the problems of large subjectivity and inaccurate diagnosis results in the fault diagnosis of tank gun control system, the fault diagnosis method based on improved artificial bee colony is studied. Combined with the improved artificial bee colony algorithm and BP neural network, a BP neural network algorithm based on improved bee colony optimization algorithm is formed and the model of the algorithm is established. And through the use of MATLAB simulation of computer programs, compared with the BP neural network algorithm without optimization, the experiment is summarized. The results show that the system can give fault diagnosis results more accurately, which helps to improve the maintenance efficiency and reliability of the tank gun control system.