BDS-3 satellite clock bias prediction based on particle swarm neural network

Aiming at the instability of wavelet neural network prediction results in clock bias prediction, a clock bias prediction model based on particle swarm optimization wavelet neural network is proposed to improve the stability of prediction results. In the model, each threshold and weight of the wavelet neural network is taken as the position vector of the particle, and the optimal value of the threshold and weight of the network is sought by the particle swarm optimization algorithm, which reduces the probability of local extreme value of the network, and thus improves the stability of the prediction results of the wavelet neural network. The effectiveness of the model is verified by analyzing the variation curve of the fitness value of the optimal individual of PSO, comparing the training error of WNN model before and after PSO optimization, and multiple prediction results of the two models. Compared with two common models, quadratic polynomial (QP) and grey (GM (1,1)), the experimental results show that the accuracy of the proposed method is improved by 49.7% and 66% respectively, the accuracy of the predicted clock bias is improved by 97.7% compared with the ultra-fast clock bias product, and the quality of the predicted clock bias is obviously better than that of the ultra-fast clock bias product. © 2023 Editorial Department of Journal of Chinese Inertial Technology. All rights reserved.