| 神经网络在GPS高程拟合中的应用 |
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| 引用本文： | 曾凯,姜岩,吴玮. 神经网络在GPS高程拟合中的应用[J]. 黑龙江工程学院学报, 2013, 27(3): 12-16 |
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| 作者姓名： | 曾凯 姜岩 吴玮 |
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| 作者单位： | 1. 山东科技大学测绘学院,山东青岛,266510
2. 青岛秀山移动测量有限公司,山东青岛,266510 |
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| 摘 要： | 讨论利用遗传算法(GA)、粒子群算法(PSO)来优化BP神经网络权值和阈值的原理；结合平坦地区的工程实例,研究二次曲面、BP、GA-BP与PSO-BP 4种拟合模型在GPS高程拟合中的应用.拟合结果表明:PSO算法优化BP神经网络精度效果优于GA算法优化BP神经网络精度,拟合误差更小.
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| 关 键 词： | 高程拟合 GPS 遗传算法 BP神经网络 粒子群算法 |
Application of neural network to GPS elevation fitting |
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| ZENG Kai , JIANG Yan , WU Wei. Application of neural network to GPS elevation fitting[J]. Journal of Heilongjiang Institute of Technology, 2013, 27(3): 12-16 |
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| Authors: | ZENG Kai JIANG Yan WU Wei |
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| Affiliation: | 1. Institute of Surveying and Mapping, Shandong University of Science and Technology, Qingdao, 266590, China 2. Qingdao Xiushan Mobile Measuring Co. Ltd. , Qingdao 266590, China) |
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| Abstract: | The genetic algorithms（GA） and particle swarm optimization（PSO） are discussed to optimize BP neural weights and threshold principle. Combined with flat areas of the engineering practice, the four fitting models of quadratic surface, BP, GA-BP and PSO-BP are researched to be applied to the GPS elevation. The fitting results show that the PSO algorithm optimizing BP neural network accuracy is better than GA algorithm optimizing BP neural network accuracy, of which the fitting error is smaller. |
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| Keywords: | elevation fitting GPS genetic algorithm BP neural network particle swarm algorithm |
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