基于SCSO-BP神经网络的卫星姿态控制系统故障预测

Satellite Attitude Control System Fault Prediction Based on SCSO-BP Neural Network

近年来,随着人工智能的迅速发展,基于人工神经网络的卫星姿态控制系统故障预测方法得到了越来越多的重视。在反向传播(Back Propagation,BP)神经网络中,权重和偏置是重要的可调节参数,与神经网络的预测性能密切相关。BP神经网络的初始权重和偏置为随机生成,设置不当容易导致网络在训练过程中陷入局部极值,进而影响预测性能。为了提高BP神经网络的预测性能,提出了一种将沙猫群优化(Sand Cat Swarm Optimization,SCSO)算法与BP神经网络相结合的预测方法。在训练过程中,首先通过SCSO算法对BP神经网络权重和偏置进行预训练,在此基础上,利用精调后的BP神经网络对卫星姿态控制系统周期渐变故障数据的未来趋势进行预测。实验结果表明,与原始BP神经网络预测方法相比,SCSO-BP预测方法能够有效减小预测误差,具有更好的预测精度。

In recent years,with the rapid development of artificial intelligence,the fault prediction method of satellite attitude control systems based on artificial neural networks has received more and more attention.In Back Propagation(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 network,a prediction method combining the Sand Cat Swarm Optimization(SCSO)algorithm and the BP neural network is proposed.First,the SCSO algorithm is used to pre-train the weight and bias of the BP neural network during 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 prediction method can effectively reduce the prediction error and have better prediction accuracy compared with the original BP neural network prediction method.