基于SSA-ANFIS模型的BDS-3卫星钟差短期预报OA北大核心CSTPCD
Short-Term Prediction of BDS-3 Satellite Clock Bias Based on SSA-ANFIS Model
针对卫星钟差时间序列具有非线性和非平稳的特性,以及趋势分量与随机分量相互干扰可能会影响预报精度的问题,提出一种以奇异谱分析(singular spectrum analysis,SSA)为基础,融合自适应模糊神经网络(adaptive neuro-fuzzy inference system,ANFIS)的卫星钟差预报模型 SSA-ANFIS.首先利用 SSA 对钟差一次差序列进行分解和重构,从而得到趋势项和残差项;然后,使用ANFIS对重构分量进行预报,并将预报结果叠加还原,得到最终预报钟差值;最后,通过实验对比SSA-ANFIS与GM、QP、LSTM和ANFIS模型的预报效果.结果表明,相较于LSTM和ANFIS模型,该模型预报精度分别提高25.7％～40.7％和39.4％～45.7％.
In view of the non-linearity and non-stationary characteristics of satellite clock bias(SCB)time series,as well as the interference between trend and noise components that may affect the accura-cy of prediction,this paper proposes a SCB prediction model(SSA-ANFIS)based on singular spectrum analysis(SSA)and adaptive neuro-fuzzy inference system(ANFIS).This paper first uses SSA to de-compose and reconstruct the first-order difference sequence of clock bias,obtaining the trend compo-nent and the residual component.Then,it uses the ANFIS model to predict the reconstructed compo-nents,and superimposes and restores the predicted results to obtain the final predicted clock bias val-ue.Finally,through experiments,this paper compares the proposed model with GM,QP,LSTM and ANFIS models.The results show that SSA-ANFIS model can effectively improve the prediction accu-racy of the single model.Compared with the LSTM and ANFIS models,its prediction accuracy in-creased by 25.7％-40.7％and 39.4％-45.7％,respectively.
蔡成林;吴明杰;吕开慧
湘潭大学自动化与电子信息学院,湖南省湘潭市北二环,411105湘潭大学数学与计算科学学院,湖南省湘潭市北二环,411105
测绘与仪器
卫星钟差奇异谱分析自适应模糊神经网络模型钟差预报
satellite clock biassingular spectrum analysis(SSA)adaptive neuro-fuzzy inference sys-tem(ANFIS)clock bias prediction
《大地测量与地球动力学》 2024 (009)
926-931 / 6
国家重点研发计划(2020YFA0713501). National Key Research and Development Program of China,No.2020YFA0713501.
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