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| 基于神经网络的GPS高程拟合方法优选及精度分析 | |
| 引用本文： | 强明,郭春喜,周红宇.基于神经网络的GPS高程拟合方法优选及精度分析[J].重庆交通大学学报(自然科学版),2012,31(4):815-818. |
| 作者姓名： | 强明 郭春喜 周红宇 |
| 作者单位： | 1. 西安科技大学测绘科学与技术学院,陕西西安,710054 2. 陕西省测绘局大地测量数据处理中心,陕西西安,710054 3. 重庆交通大学土木建筑学院,重庆,400074 |
| 摘 要： | 针对现有的几种神经网络GPS高程拟合方法,讨论了利用遗传算法(GA)、粒子群算法(PSO)优化BP神经网络权值和阀值的原理;结合分布较均匀、现势性较好的GPS和水准联测数据,试算了基于神经网络的GPS高程拟合。拟合结果表明:基于PSO算法优化的BP神经网络的拟合精度优于GA算法,误差相对更小。 |
| 关 键 词： | 遗传算法 BP神经网络 径向基神经网络 粒子群优化算法 |
Optimization and Precision Evaluation with GPS Elevation Fitting Method Based on Neural Network |
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| Qiang Ming , Guo Chunxi , Zhou Hongyu.Optimization and Precision Evaluation with GPS Elevation Fitting Method Based on Neural Network[J].Journal of Chongqing Jiaotong University,2012,31(4):815-818. | |
| Authors: | Qiang Ming Guo Chunxi Zhou Hongyu |
| Institution: | 1.College of Geomatics,Xi’an University of Science & Technology,Xi’an 710054,Shaanxi,China; 2.Geodetic Survey Data Processing Center,Shaanxi Bureau of Surveying & Mapping,Xi’an 710054,Shaanxi,China; 3.School of Civil Engineering & Architecture,Chongqing Jiaotong University,Chongqing 400074,China) |
| Abstract: | According to current elevation fitting methods of networks,the genetic algorithms(GA) and particle swarm optimization(PSO) methods were employed to optimization of the weights and threshold of BP neural networks;with evenly distributed GPS data,GPS elevation fitting based on neural network is calculated.The fitting results show that optimization of the BP neutral network by PSO is better than that by GA and the error is relatively small. |
| Keywords: | genetic algorithms(GA) BP neural network RBF particle swarm optimization(PSO) |
| 本文献已被 CNKI 万方数据 等数据库收录！ | |