地理空间信息2025，Vol.23Issue(2)：108-111,4.DOI:10.3969/j.issn.1672-4623.2025.02.024
改进海鸥算法优化BP神经网络的GPS高程拟合方法
GPS Elevation Fitting Method Using Improved the Seagull Algorithm to Optimize BP Neural Network
伍勇1
作者信息
- 1. 湖北省荆州市水文水资源勘测局,湖北 荆州 434000
- 折叠
摘要
Abstract
The initial weight and threshold of parameters will affect the elevation fitting of BP neural network,and the improved seagull algo-rithm can make up the defects of BP neural network,so that the fitting model can get better parameters.In this paper,we added seagull algorithm into the initial population of chaotic mapping and changed the inertia weights movement to improve the algorithm's accuracy,convergence speed and robustness.We used the optimal adaptive individual neuron connection weights and thresholds of improved seagull algorithm to establish the prediction model of GPS elevation anomalies by using improved seagull algorithm to optimize BP neural network,and checked the model by the actual engineering GPS elevation data.The model is checked by actual engineering GPS elevation data.The results show that the improved mod-el proposed in this paper has higher fitting accuracy and stability than the traditional BP neural network model,and has better adaptability to the data,which is a reliable fitting model.关键词
海鸥算法/混沌映射/BP神经网络/高程拟合Key words
seagull algorithm/chaotic mapping/BP neural network/elevation fitting分类
天文与地球科学引用本文复制引用
伍勇..改进海鸥算法优化BP神经网络的GPS高程拟合方法[J].地理空间信息,2025,23(2):108-111,4.
