| 遗传模拟退火算法优化BP神经网络的GPS高程拟合 |
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| 引用本文： | 石晨阳,袁晓燕,江志成.遗传模拟退火算法优化BP神经网络的GPS高程拟合[J].全球定位系统,2021,46(5):55-59. |
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| 作者姓名： | 石晨阳 袁晓燕 江志成 |
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| 作者单位： | 重庆交通大学，重庆 400074 |
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| 基金项目： | 广西空间信息与测绘重点实验室资助课题（19-050-11-03） |
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| 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.
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| 关 键 词： | GPS 高程异常 BP神经网络 遗传算法(GA) 模拟退火算法(SA) |
| 收稿时间： | 2021-04-09 |
GPS elevation fitting of BP neural network optimized by genetic simulated annealing algorithm |
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| Affiliation: | Chongqing Jiaotong University, Chongqing 400074, China |
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| Abstract: | 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. |
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