GNSS Height Anomaly Fitting Method Based on MVO Optimized Neural Network
Abstract We are concerned with the problem of gradient vanishing and ease of falling into local extremum of ordinary neural network. To enable the neural network prediction model to be more accurate in prediction, we apply the global optimizing feature of multi-verse optimizer(MVO) to retrieve the reliable neuron threshold and connection weight between each layer of BP neural network. We build the prediction model of GNSS height anomaly fitting based on the MVO-BP method, then we carry out the feasibility test of the algorithm by adopting a limited amount of height anomaly data in practical engineering. The results show that MVO-BP method is more accurate and versatile than the conventional BP neural network method and the multifaceted function method, and it has a certain reference value for the acquisition of normal height in practical engineering measurements.
Key words :
BP neural network
multi-verse optimizer(MVO)
GNSS
height anomaly fitting
Cite this article:
MENG Jinlong,TANG Shihua,ZHANG Yan et al. GNSS Height Anomaly Fitting Method Based on MVO Optimized Neural Network[J]. jgg, 2022, 42(12): 1233-1238.
MENG Jinlong,TANG Shihua,ZHANG Yan et al. GNSS Height Anomaly Fitting Method Based on MVO Optimized Neural Network[J]. jgg, 2022, 42(12): 1233-1238.
URL:
http://www.jgg09.com/EN/ OR http://www.jgg09.com/EN/Y2022/V42/I12/1233
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