A Health Prediction Method of Fire Control Systems Based on IPSO-RF
The fire control system is one of core parts of the tank,which plays an important role in im-proving tank firepower and survivability on the battlefield,so health prediction for the fire control sys-tem is particularly important.In order to improve the prediction accuracy,a health prediction method of fire control systems based on the improved particle swarm optimization algorithm and random forest algorithm(IPSO-RF algorithm)was proposed.First,the adaptive weight was introduced to improve the particle swarm optimization algorithm.Then the core parameters of the random forest algorithm were optimized and the health prediction model was established.Finally,the power module of the fire con-trol computer and sensor subsystem was selected as the experimental object and compared with LSTM,GSM-SVM,BAYES-RF and GSM-RF models.The experimental results show that the proposed method is superior to other models in terms of prediction accuracy and effect.
health predictionrandom forestparticle swarm optimizationfire control system
万方数据
维普
