基于模糊Hopfield神经网络的大气降水粒子分类

提出基于模糊Hopfield神经网络的大气降水粒子分类,它是通过计算样本间的模糊关系的相似程度,再加上Hopfield网络所特有的高稳定性,对大气降水粒子进行分类.通过对各降水粒子所对应雷达回波数据的分析,验证了方法的可行性.     

基于ALR的Hopfield神经网络在高程拟合中的应用

运用具有正规化项的增广拉格朗日函数作为神经网络的能量函数,辅助二次曲面拟合,进一步探索Hopfield神经网络在高程拟合中的应用。实际算例表明,该方法可以大大提高神经网络的计算效率和可靠性。     

边坡变形破坏模式识别系统的实现

以岩体结构为最基本的控制因素,同时结合考虑岩性、结构面形态及边坡结构参数等因素实现了边坡变形破坏模式识别系统。系统将模式识别技术与专家系统有机结合,针对五种岩体类型边坡分别进行模式判断;具有与工程结合紧密、能获得定量结果、层次清晰及使用方便等优点。由本系统判断识别的破坏模式与实际破坏模式相符合,说明了系统的实用性和可行性。     

基于Hopfield神经网络模型的遥感影像分类算法

针对遥感影像的分类特点，提出了一种基于Hopfield神经网络模型的遥感影像分娄算法。首先阐述了Hopfield神经网络的结构及其工怍原理，分析了Hopfield神经网络优化规则；然后在Hopfield神经网络通用模型基础上，实现了Hopfield神经网络的算法。实验结果表明，这种分类器具有较高的精度与效率，分类结果优于最大似然分类法。     

A spatial–temporal Hopfield neural network approach for super-resolution land cover mapping with multi-temporal different resolution remotely sensed images

The mixed pixel problem affects the extraction of land cover information from remotely sensed images. Super-resolution mapping (SRM) can produce land cover maps with a finer spatial resolution than the remotely sensed images, and reduce the mixed pixel problem to some extent. Traditional SRMs solely adopt a single coarse-resolution image as input. Uncertainty always exists in resultant fine-resolution land cover maps, due to the lack of information about detailed land cover spatial patterns. The development of remote sensing technology has enabled the storage of a great amount of fine spatial resolution remotely sensed images. These data can provide fine-resolution land cover spatial information and are promising in reducing the SRM uncertainty. This paper presents a spatial–temporal Hopfield neural network (STHNN) based SRM, by employing both a current coarse-resolution image and a previous fine-resolution land cover map as input. STHNN considers the spatial information, as well as the temporal information of sub-pixel pairs by distinguishing the unchanged, decreased and increased land cover fractions in each coarse-resolution pixel, and uses different rules in labeling these sub-pixels. The proposed STHNN method was tested using synthetic images with different class fraction errors and real Landsat images, by comparing with pixel-based classification method and several popular SRM methods including pixel-swapping algorithm, Hopfield neural network based method and sub-pixel land cover change mapping method. Results show that STHNN outperforms pixel-based classification method, pixel-swapping algorithm and Hopfield neural network based model in most cases. The weight parameters of different STHNN spatial constraints, temporal constraints and fraction constraint have important functions in the STHNN performance. The heterogeneity degree of the previous map and the fraction images errors affect the STHNN accuracy, and can be served as guidances of selecting the optimal STHNN weight parameters.     

由Saastamoinen和Hopfield模型推算水汽含量的比较与分析

详细介绍利用ＧＰＡ数据推算大气水汽含量的过程。根据Ｓａａｓｔａｍｏｉｎｅｎ和Ｈｏｐｆｉｅｌｄ两种模型利用实测数据对大气水汽含量进行了推算,并对其结果进行了比较和分析,得到了一些有益的结论。     

An linear matrix inequality approach to global synchronisation of non-parameter perturbations of multi-delay Hopfield neural network

In this study, a successful linear matrix inequality approach is used to analyse a non-parameter perturbation of multi-delay Hopfield neural network by constructing an appropriate Lyapunov-Krasovskii functional. This paper presents the comprehensive discussion of the approach and also extensive applications.     

单站地基GPS天顶延迟反演大气剖面研究

基于一定的大气折射率剖面模型和搜索方法,可以由GPS天顶延迟反演大气折射率剖面。利用上海气象站的探空数据和上海IGS站的GPS数据,对基于单站地基GPS天顶延迟的大气折射率剖面反演方法进行了验证。结果表明,根据目前处理得到的天顶延迟,反演剖面与实际剖面吻合较好。     

基于神经网络的点状地图符号识别

地图符号识别是地图成图自动化的一个重要研究方向,但是传统的地图符号识别方法已经不能满足数字地图发展的需要,迫切需要一种用自动化技术代替人工数字化的作业方式[1].因此地图符号的识别是整个技术的关键,而点状地图符号的识别更是地图符号识别的基础.本文探讨和研究了基于神经网络的点状地图符号识别.     

基于Hopfield神经网络的中国近40年气候要素时空变化分析

利用1961~2000年中国大部分省区(香港、澳门、台湾、海南地区数据暂缺)194个气象站点逐日降水量、气温和相对湿度数据,通过克里格插值、Hopfield神经网络聚类以及方差分析,对中国气候变化的时空特征进行分析。分析结果表明:中国以增温为主导趋势,其次是多雨趋势;东部地区出现变干趋势,而西部地区在逐渐增湿;对3种要素进行聚类分析,并利用方差分析检验差异性是否显著,最后分析了聚类结果变化趋势,结果与插值分析一致,说明克里格插值结果的可信性。表明在全球增温的驱动下,中国气候变化格局处于调整状态,湿润地区干旱化,干旱地区变得湿润。