基于人工鱼群优化LS-SVM的卫星钟差预报

针对导航卫星短期钟差预报精度不高的问题,提出了一种基于人工鱼群(AFSA)优化最小二乘支持向量机(LS-SVM)的卫星钟差预报方法。利用人工鱼群算法较强的全局寻优能力优化LS-SVM模型的惩罚参数和核宽度参数,避免人为选择参数的盲目性,提高了LS-SVM的泛化能力和预报精度。选取IGS产品中4颗典型卫星的钟差数据,分别采用人工鱼群优化LS-SVM模型、神经网络模型和灰色系统模型进行短期钟差预报,计算结果表明:人工鱼群优化LS-SVM模型的预报精度优于其它2种模型,尤其是在铷钟方面,预报误差在0.5 ns内,运行时间在5 min内。

Satellite Clock Error Forecast Based on AFSA Optimization LS-SVM

Aimed at the poor performance of short term prediction of navigation satellite clock error, a method is proposed for prediction of satellite clock error based on the least square support vector machine (LS-SVM) and artificial fish-swarm algorithm (AFSA). To avoid the man-made blindness and enhance the efficiency of online forecasting, penalty parameter and kernel bandwidth parameter of LS-SVM are optimized by artificial fish-swarm algorithm with a rather good ability of global optimization based on AFSA model. The clock data of four typical GPS satellites are chosen and respectively used in three models to forecast short term clock error. The results show that the accuracy of LS-SVM based on model is superior to the other models, especially in the field of rubidium clock; the error is less than 0.5 ns, and running time is in 5 minutes. The work provides a new way for short term prediction of satellite clock error.