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841.
842.
本文在研究BP神经网络和模糊理论的基础上,提出了传统BP算法的一种改进方法和基于模糊系统的神经网络遥感影像分类方法。通过试验表明:基于模糊技术的神经网络分类方法要优于BP神经网络方法,取得了令人满意的效果。 相似文献
843.
844.
结合本公司实施的一个大桥施工控制网测量项目,详细介绍了利用GPS进行大型桥梁施工控制网布设的方法,从方案比选、选点埋石、坐标系和投影面选择、测量过程中的质量控制措施、控制网的复测与维护等方面做了较为详细的介绍。 相似文献
845.
Steffen Schön 《GPS Solutions》2007,11(2):107-117
GPS is a promising tool for real-time monitoring of deformations of slopes or large structures. However, remaining systematic
effects in GPS phase observations after double differencing and application of a priori models affect the resulting coordinates.
They complicate the proper separation of the actual deformations from pseudo-deformations induced by the systematic effects.
This paper shows that for small monitoring networks (baseline lengths <5 km) only affine distortions of the network geometry
are generated by the remaining distance dependent systematic effects, e.g. unmodelled tropospheric and ionospheric propagation
effects, or satellite orbit errors. Hence, a generic correction model is given by a three-dimensional affine transformation
involving a maximum of 12 transformation parameters. For the determination of these parameters, four high quality GPS stations
are necessary which are not affected by the actual deformations to be monitored. Based on the analysis of network geometries
of synthetic GPS networks with large height differences and considering the physics of the GPS observations it is shown, however,
that less than 12 parameters are sufficient for the computation of the corrections. The proposed 8 parameter model was applied
to the GPS monitoring network of the Gradenbach landslide. For this small network with large height differences, it was shown
that the distortions can be reduced by about 75%. 相似文献
846.
针对三维激光测高拟合问题,本文采用BP神经网络进行拟合,该方法可以有效地消除或减弱数据中的系统误差影响,提高计算精度。实际算例证明,在数据无粗差情况下,BP神经网络的内符合与外符合精度优于传统的二次曲面拟合的精度。 相似文献
847.
848.
849.
850.
Studies on small-world networks have received intensive interdisciplinary attention during the past several years. It is well-known
among researchers that a small-world network is often characterized by high connectivity and clustering, but so far there
exist few effective approaches to evaluate small-world properties, especially for spatial networks. This paper proposes a
method to examine the small-world properties of spatial networks from the perspective of network autocorrelation. Two network
autocorrelation statistics, Moran’s I and Getis–Ord’s G, are used to monitor the structural properties of networks in a process of “rewiring” networks from a regular to a random
network. We discovered that Moran’s I and Getis–Ord’s G tend to converge and have relatively low values when properties of small-world networks emerge. Three transportation networks
at the national, metropolitan, and intra-city levels are analyzed using this approach. It is found that spatial networks at
these three scales possess small-world properties when the correlation lag distances reach certain thresholds, implying that
the manifestation of small-world phenomena result from the interplay between the network structure and the dynamics taking
place on the network.
相似文献