摘要
针对传统BP神经网络收敛速度慢、易陷入局部最优和遗传算法优化BP神经网络(GA-BP)算法过早收敛的问题,提出了遗传模拟退火算法优化BP神经网络(GSA-BP)算法.在遗传算法(GA)的种群更新中加入模拟退火算法(SA),保留种群的多样性.用GSA-BP算法对某地区进行高程异常拟合,并与BP算法和GA-BP算法结果进行比较.结果显示:GSA-BP算法精度可分别提高约51%、25%,速度提高约77%、39%,且能基本满足四等水准测量精度要求.该方法在GPS高程拟合中具有可行性.
In order to solve the problems of slow convergence speed of traditional BP neural network,easy to fall into local optimum and premature convergence of genetic algorithm optimized BP neural network(GA-BP)algorithm,a genetic simulated annealing algorithm optimized BP neural network(GSA-BP)algorithm was proposed.The simulated annealing algorithm(SA)was added to the genetic algorithm(GA)to keep the diversity of the population.GSA-BP algorithm is used to fit the elevation anomaly in a certain area,and the results are compared with BP algorithm and GA-BP algorithm.The results show that the GSA-BP algorithm can improve the accuracy by 51%and 25%,and the speed by 77%and 39%respectively,and can basically meet the requirements of the fourth grade leveling accuracy.This method proves to be feasible in GPS elevation fitting.
作者
石晨阳
袁晓燕
江志成
SHI Chenyang;YUAN Xiaoyan;JIANG Zhicheng(Chongqing Jiaotong University,Chongqing 400074,China)
出处
《全球定位系统》
CSCD
2021年第5期55-59,共5页
Gnss World of China
基金
广西空间信息与测绘重点实验室资助课题(19-050-11-03)。