GNSS Height Fitting Based on Improved RBF Neural Network
Abstract In the traditional RBF neural network model in the GNSS height fitting, the fitting accuracy is relatively low, the stability is relatively poor, and the correlation factors need to be set artificially in advance. This paper adopts the improved adaptive weight particle swarm optimization algorithm and MATLAB RBF newrb. The network function newrb combines to realize the automatic optimization of the number of hidden nodes and SPREAD in the RBF neural network function model, and improve the accuracy and stability of the algorithm in GNSS height fitting. Through the example analysis, the method has high fitting precision and can reach mm precision. Compared with the traditional quadratic polynomial model, the accuracy is improved by 17% and the stability is good. It has important reference value for accurately solving GNSS height anomaly.
Key words :
GNSS
height fitting
improved particle swarm optimization
RBF neural network
MATLAB
Cite this article:
YUAN Debao,ZHANG Jian,ZHAO Chuanwu et al. GNSS Height Fitting Based on Improved RBF Neural Network[J]. jgg, 2020, 40(3): 221-224.
YUAN Debao,ZHANG Jian,ZHAO Chuanwu et al. GNSS Height Fitting Based on Improved RBF Neural Network[J]. jgg, 2020, 40(3): 221-224.
URL:
http://www.jgg09.com/EN/ OR http://www.jgg09.com/EN/Y2020/V40/I3/221
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