Elman Neural Network Based on Particle Swarm Optimization for Prediction of GPS Rapid Clock Bias

Abstract

To improve the accuracy of the satellite rapid clock bias, a modified Elman neural network clock bias prediction method based on particle swarm optimization (PSO) algorithm is proposed. The Elman recurrent neural network is introduced to predict the clock bias, its weights and thresholds are improved by PSO algorithm to improve the training speed and prediction accuracy. Then, the optimization method is applied to the rapid clock bias prediction, and the steps of using this method for the rapid clock bias prediction are given. Finally, the optimization method is compared with common quadratic polynomial model, gray model and ultra rapid clock bias product IGU-P. The results show that the PSO-Elman model achieves high accuracy and stability for four different types of GPS satellite clock, and its prediction accuracy and stability improved by 85%, 74%, 89% and 71%, 53%, 28% compared with QPM, GM(1,1) and IGU- P products, respectively.