基于广播星历和神经网络的GPS卫星坐标计算方法

通过在开普勒轨道模型中加入神经网络算法,得到一种全新的卫星坐标计算的混合模型:基于广播星历和神经网络混合模型,从而提高了利用广播星历计算卫星坐标的精度。计算结果表明:混合模型较传统的开普勒轨道模型在X、Y、Z3个方向上卫星坐标计算的精度提高了约10%。     

高速公路路基沉降预测的神经网络模型

软土地基的沉降控制是保证高速公路建设质量的一个关键技术.论文主要介绍了一个对高速公路路基沉降进行预测的神经网络模型.对神经网络的BP算法进行了改进,提高了BP算法的学习收敛速度和网络性能的稳定性.神经网络法预测路基沉降的难点之一是合适的训练样本构造问题,论文提出了新颖独特的“训练样本”构造方法,且应用效果良好.利用路基沉降量实测资料直接建模,采用BP网络计算的改进算法,可较为准确地预测大约4个月之后的沉降量,预测值与实测值吻合较好.     

基于人工神经网络的赤潮卫星遥感方法研究

根据赤潮的卫星遥感探测机理，应用人工神经网络技术，建立和利用NOAA AVHRR可见光和热红外波段遥感数据的BP神经网络赤潮信息提取模型。应用实例显示。基于该人工神经网络方法可以提取赤潮发生地点和范围等信息，赤潮探测正确率达到78.5%。研究结果表明，应用人工神经网络方法提取赤潮信息是可行的。本文中建立的BP赤潮信息提取模型适当修改后可移植应用于其它传感器遥感数据进行赤潮信息提取。     

SPOT卫星影像居民地信息自动提取的决策树方法研究

以南京市江宁县为研究区域，首先分析了该区域居民地的影像特征，然后研究了居民地及其背景地物在SPOT—4(Ⅺ)卫星影像4个波段上的光谱特征，并由此探讨了它们在光谱特征上的可分性。研究发现，除道路在光谱特征上与居民地差异不大而难以完全分开外，其它背影地物均可以依据各波段亮度值的大小关系或适当的阈值与居民地分开。但道路和居民地在形状上存在明显差异，因此可以利用形状指数的差异加以去除。最后分析建立了基于光谱特征和形状特征的简单决策树模型，对研究区域居民地信息进行了提取并对结果进行了精度评价。结果表明，该方法的总体提取效果较好，特别是对于面积大于10000m^2的城镇和集村。其提取精度与通常的监督分类方法相比有了很大的提高，只是在水际交界处和道路两侧有误判现象。因此，利用该模型可以将背景地物类型复杂的江南地区的城镇和集村居民地自动提取出来，并且模型受时相影响较小，只是在域值大小上会存在一些差异。     

三层BP网隐层节点数确定方法的研究

对确定三层BP网络隐层节点数的理论依据和现有做法进行了研究,提出了一种三层BP网络隐层节点数的双向确定法     

Improving neural network performance on the classification of complex geographic datasets

Neural Networks are now established computational tools used for search minimisation and data classification. They offer some highly desirable features for landuse classification problems since they are able to take in a variety of data types, recorded on different statistical scales, and combine them. As such, neural networks should offer advantages of increased accuracy. However, a barrier to their general acceptance and use by all but `experts' is the difficulty of configuring the network initially.  This paper describes the architectural problems of applying neural networks to landcover classification exercises in geography and details some of the latest developments from an ongoing research project aimed at overcoming these problems. A comprehensive strategy for the configuration of neural networks is presented, whereby the network is automatically constructed by a process involving initial analysis of the training data. By careful study of the functioning of each part of the network it is possible to select the architecture and initial weights on the node connections so the constructed network is `right first time'. Further adaptations are described to control network behaviour, to optimise functioning from the perspective of landcover classification. The entire configuration process is encapsulated by a single application which may be treated by the user as a `black box', allowing the network to the applied in much the same way as a maximum likelihood classifier, with no further effort being required of the user.     

Using computational intelligence techniques to model subglacial water systems

Measurements of water pressure beneath Trapridge Glacier, Yukon Territory, Canada show that the basal water system is highly heterogeneous. Three types of behaviour were recorded: pressure records which are strongly correlated, records which are strongly anticorrelated, and records which alternate between strong correlation and strong anticorrelation. We take the pressure in bore-holes that are connected to the evacuation route for basal water as the forcing, and the other pressures as the response to this forcing. Previous work (Murray and Clarke 1995) has shown that these relationships can be modelled using low-order nonlinear differential equations optimized by inversion. However, despite optimizing the model parameters we cannot be sure that the final model forms are themselves optimal. Computational intelligence techniques provide alternative methods for fitting models and are robust to missing or noisy data, applicable to non-smooth models, and attempt to derive optimal model forms as well as optimal model parameters. Four computational intelligence techniques have been used and the results compared with the more conventional mathematical model. These methods were genetic programming, artificial neural networks, fuzzy logic and self-organizing maps. We compare each technique and offer an evaluation of their suitability for modelling the pressure data. The evaluation criteria are threefold: (1) goodness of fit and an ability to predict subsequent data under different surface weather conditions; (2) interpretability, and the extent and significance of any new insights offered into the physics of the glacier; (3) computation time. The results suggest that the suitability of the computational intelligence techniques to model these data increases with the complexity of the system to be modelled.     

用神经网络方法确定测区似大地水准面

本文提出一种用神经网络确定测区似大地水准面的方法,并给出了相应的神经网络结构和算法。研究了一个测区中用该方法确定的似大地水准面的精度,结果表明用该方法确定的似大地水准面的精度为厘米级,并与二次多项式曲面拟合似大地水准面的方法作了比较,神经网络方法的精度较优。     

基于尺度空间的分层聚类方法及其在遥感影像分类中的应用

基于尺度空间的分层聚类方法（ＳＳＨＣ）是一种以热力学非线性动力机制为理论基础的新型聚类算法,是视觉松驰化过程的模拟。与传统基于统计方法的聚类算法相比较,ＳＳＨＣ具有样本空间可服从自由分布、通过规则可获取最优聚类中心点及类别、可在聚类过程中融合后验知识等优点。本文从聚类和热力学运动机制和视觉模拟过程出发,对ＳＳＨＣ聚类算法进行综合分析,并对如何生成聚类树的过程进行详细描述邮通过融合点的部分自由能进行     

粗糙神经网络在森林火灾预警中的应用

利用神经网络和粗糙集处理不确定性问题的优势，提出一种粗糙集结合神经网络进行森林火灾预测模型。通过与传统预测模型相比较，证明了该方法的有效性。