基于对应分析的训练样本的选择

本文提出一种基于对应分析的训练样本的选择方法。它从训练样本中自动地选择有代表性的典型训练样本,使得在自动分类中充分利用所采集的样本信息,以便得到满意的分类结果。通过实验与分析证明,该方法是可行的,它明显优于人工随机选择训练样本的方式。与基于Q型因子分析的训练样本选择方法相比,可以更快地得到较少的典型样本,满意的分类精度。     

基于神经网络的遥感影像分类方法研究

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。     

Administrative division system changes and urbanization in China

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基于RS与GIS的干旱区绿洲景观格局变化研究——以金塔绿洲为例

景观生态学是宏观生态研究的一个新领域。景观格局变化研究,是目前景观生态学的研究热点。利用遥感的手段获取试验区1990和2000年景观格局的基础数据,并利用GIS做分析的工具,计算出6个指标值,采用格网化的方法,将研究区域的景观斑块转化成5m×5m的格网,进行空间分析,计算得出了景观斑块的转移矩阵,为景观格局在空间上的变化分析打下基础。分析显示:①多样性、均匀性、优势度3个指标数值上有很大的相关性,只需考虑多样性;②绿洲景观格局的变化与水资源的变化密切相关;③绿洲有沙漠化的威胁;④城镇居民点用地扩展迅速,主要在未利用土地上发展,金塔绿洲内城镇居民区有城市化的趋势;⑤城镇居民点破碎化指标值未能真正体现其景观特性,当不考虑少数几个大的斑块时,能够得到较为理想的结果。     

PROGRAM FOR PROCESSING OF HISTORICAL LABORATORY DATA BASED ON FUZZY SET THEORY AS A TOOL FOR DECISION SUPPORT

A computer program(FEA)is presented for processing historical laboratory data.It performs on a list ofsample entries stored in a laboratory information management system.Using an algorithm which is basedon fuzzy set theory,FEA classifies the entries into a limited number of clusters called sample types.Theclassification is fully user-defined.The program transforms the historical data into a representation whichis more suitable for studying the performance of the laboratory or which can be used as preparation for asimulation project.     

MARS:A TUTORIAL

This tutorial paper presents a simplified view of one of the more recently published multivariatecalibration methods particularly suited to dealing with non-linear data sets.The method is referred toas MARS and stands for multivariate adaptive regression splines.Simple examples are provided toexplain the workings of the method.     

南极乔治王岛阿德默勒尔蒂湾火山活动时空演化

在乔治王岛中部的阿德默勒尔蒂湾 ,凯勒半岛、于尔曼山嘴和埃内坎角是第三纪火山岩集中分布区。野外地质调查和同位素年代学研究表明 ,凯勒半岛发生了三期火山喷发 (三个火山喷发旋回 ) ,同时伴随着火山活动中心的不断北移 ;在第二期火山喷发之后 ,火口塌陷 ,形成破火山 ,之后在半岛北部又发生火山喷发 ,并向东迁移到于尔曼山嘴。因此凯勒半岛是一复活破火山 ,火山活动具有随时间迁移的特性。埃内坎角火山活动虽与上述两地区大致同时 ,但化学成分上不存在明显演化关系 ,应属另一相对独立的火山活动中心     

STUDY ON MIXED MODEL OF NEURAL NETWORK FOR FARMLAND FLOOD/DROUGHT PREDICTION

The paper concerns a flood/drought prediction model involving the continuation of time seriesof a predictand and the physical factors influencing the change of predictand.Attempt is made toconstruct the model by the neural network scheme for the nonlinear mapping relation based onmulti-input and single output.The model is found of steadily higher predictive accuracy by testingthe output from one and multiple stepwise predictions against observations and comparing theresults to those from a traditional statistical model.     

Adjoint Matching Condition for Parameterized Discontinuities—A Derivation Using Lagrangian-form Costfunction

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ARTIFICIAL NEURAL NETWORK CORRECTION OF ROUGH ERRORS OF OBSERVATIONS

In the context of tower measured radiation datasets.following the correction principle meetinga diagnostic equation in data quality control and in terms of a technique for model construction ondata and ANN(artificial neural network)retrieval for BP correction of radiation measurementswith rough errors available,a BP model is presented.Evidence suggests that the developed modelworks well and is superior to a convenient multivariate linear regression model,indicating its wideapplications.