地理空间信息2025，Vol.23Issue(3)：1-4,4.DOI:10.3969/j.issn.1672-4623.2025.03.001
基于改进粒子群优化神经网络的高程异常拟合方法
Elevation Anomaly Fitting Method Based on Improved Particle Swarm Optimization Neural Network
张文君 1师文杰2
作者信息
- 1. 青海省基础测绘院,青海 西宁 810000
- 2. 青海省有色第一地质勘查院,青海 西宁 810000
- 折叠
摘要
Abstract
In response to the problems of local optima and slow convergence speed of BP neural network in elevation anomaly fitting,we proposed an elevation anomaly fitting method based on improved particle swarm optimization neural network.By combining the nonlinear adjustment strategy of inertia weight factor and the linear adjustment strategy of learning factor,this method can effectively improve the convergence speed of particle swarm optimization algorithm and avoid local optima.We used the improved particle swarm optimization algorithm to optimize the initial parameters of BP neural network to improve the accuracy of elevation anomaly fitting,and verified the accuracy and effectiveness of this method by two sets of typical experimental areas.关键词
改进粒子群/自适应惯性权值/线性学习因子/BP神经网络/高程异常拟合Key words
improved particle swarm/adaptive inertia weight/linear learning factor/BP neural network/elevation anomaly fitting分类
天文与地球科学引用本文复制引用
张文君,师文杰..基于改进粒子群优化神经网络的高程异常拟合方法[J].地理空间信息,2025,23(3):1-4,4.
