In recent years,with the rapid development of artificial intelligence,the fault prediction method of satel-lite attitude control systems based on artificial neural networks has received more and more attention.In Back Prop-agation(BP)neural networks,weights and biases are important tunable parameters,which are closely related to the prediction performance of neural networks.The initial weights and biases of the BP neural network are generated by randomization,and improper settings can easily lead to the network falling into local extremes during training,which will affect the prediction performance.In order to improve the prediction performance of the BP neural net-work,a prediction method combining the Sand Cat Swarm Optimization(SCSO)algorithm and the BP neural net-work is proposed.First,the SCSO algorithm is used to pre-train the weight and bias of the BP neural network dur-ing the training process.On this basis,using the fine-tuned BP neural network to predict the future trend of periodic gradient fault data of the satellite attitude control system.The experimental results show that the SCSO-BP predic-tion method can effectively reduce the prediction error and have better prediction accuracy compared with the original BP neural network prediction method.
关键词
沙猫群优化/BP神经网络/故障预测/卫星姿态控制系统/时间序列
Key words
sand cat swarm optimization/BP neural network/fault prediction/satellite attitude control system/time series
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