RSA-BP组合模型在GNSS高程拟合中的应用  

Application of RSA-BP combined model in GNSS height fitting

作　　者：刘银涛 任超 王俊男 张炎 何广焕 LIU Yintao;REN Chao;WANG Junnan;ZHANG Yan;HE Guanghuan(College of Geomatics and Geoinformation,Guilin University of Technology,Guilin 541004,China;Guangxi Key Laboratory of Spatial Information and Geomatics,Guilin 541004,China;Guangxi Institute of Remote Sensing Information,Nanning 530023,China;School of Municipal Construction and Transportation,Guangxi Polytechnic of Construction,Nanning 530007,China)

机构地区：[1]桂林理工大学测绘地理信息学院,广西桂林541004 [2]广西空间信息与测绘重点实验室,广西桂林541004 [3]广西壮族自治区自然资源遥感院,广西南宁530023 [4]广西建设职业技术学院市政与交通学院,广西南宁530007

出　　处：《测绘通报》2023年第9期46-51,共6页Bulletin of Surveying and Mapping

基　　金：国家自然科学基金(42064003);广西高校中青年教师科研基础能力提升项目(2021KY0268);广西空间信息与测绘重点实验室基金(16-380-25-25)。

摘　　要：针对地形复杂区域构建GNSS高程异常拟合模型精度有限的问题,本文提出了一种基于爬行动物搜索算法(RSA)优化BP神经网络的方法。利用RSA对传统BP神经网络各层之间神经元的权值和阈值全局寻优,解决BP神经网络局部极值、梯度下降等问题;同时,选取三等水准测量精度以上的加密网点高程数据作为样本集,使用RSA-BP神经网络学习与训练。与最小二乘支持向量机、多面函数拟合性能对比,RSA-BP神经网络模型拟合精度最高,稳定性最好,与实际高程异常值最为吻合。Aiming at the problem that the accuracy of constructing GNSS height anomaly fitting model in a complex terrain area is limited,a method based on the reptile search algorithm(RSA)is proposed to optimize the BP neural network.The RSA is used to solve the problems of local extremes and gradient descent of BP neural networks by global optimization of the weights and thresholds of neurons between the layers of traditional BP neural networks.At the same time,the height data of encrypted points above the third class level survey accuracy were selected as the sample set and learned and trained using RSA-BP neural network.Compared with the least squares support vector machine and the multi-surface function fitting performance,the RSA-BP neural network model has the highest fitting accuracy,the best stability and the best fit with the actual height anomalies.

关 键 词：爬行动物搜索算法 BP神经网络 高程异常拟合模型 大地高 正常高 

分 类 号：P228[天文地球—大地测量学与测量工程]