黄河三角洲东营市土地利用“涨势图谱”的时空特征分析

本文在分析黄河三角洲环境生态特征的基础上 ,以 4 0年 (1 95 6～ 1 996年 )来 4期土地利用数据合成的土地利用系列图谱及其“涨势”变化图谱为基础 ,研究不同行政单元上“涨势图谱”的时空分布变化规律 ,并分析了耕地、盐碱地以及居民工矿用地在“涨势图谱”上的时空分布特征。     

Temporal Data Mining Using Genetic Algorithm and Neural Network——A Case Study of Air Pollutant Forecasts

This paper integrates genetic algorithm and neural network techniques to build new temporal predicting analysis tools for geographic information system (GIS). These new GIS tools can be readily applied in a practical and appropriate manner in spatial and temporal research to patch the gaps in GIS data mining and knowledge discovery functions. The specific achievement here is the integration of related artificial intelligent technologies into GIS software to establish a conceptual spatial and temporal analysis framework. And, by using this framework to develop an artificial intelligent spatial and temporal information analyst (ASIA) system which then is fully utilized in the existing GIS package. This study of air pollutants forecasting provides a geographical practical case to prove the rationalization and justness of the conceptual temporal analysis framework.     

人工神经网络及其在短期气候预测中的应用

从业务实际出发，提出了面向短期气候预测的神经网络。选用主分量作为网络的输入和输出，大大减少了输入输出节点数，重点突出了大尺度影响变化关系，提高了实际预报的稳定性；用全局寻优的SA算法取代经典BP算法，使网络学习训练更充分，知识获取更全面；针对业务实际设计代价函数，保证了网络学习训练能适应短期气候预测业务的要求，学习目的更明确，针对性更强。     

Sensitivity of Spatial Analysis Neural Network Training and Interpolation to Structural Parameters

A Spatial Analysis Neural Network (SANN) algorithm was applied for the analysis of geospatial data, on the basis of nonparametric statistical analysis and the concepts of traditional Artificial Neural Networks. SANN consists of a number of layers in which the neurons or nodes between layers are interconnected successively in a feed-forward direction. The Gaussian Kernel Function layer has several nodes, and each node has a transfer or an activation function that only responds (or activates) when the input pattern falls within its receptive field, which is defined by its smoothing parameter or width. The activation widths are functions of the model structural parameters, including the number of the nearest neighbor points P and a control factor F. The estimation method is based on two operational modes, namely, a training-validation mode in which the model structure is constructed and validated, and an interpolation mode. In this paper we discuss the effect of varying F and P upon the accuracy of the estimation in a two-dimensional domain for different input field sizes, using spatial data of wheat crop yield from Eastern Colorado. Crop yield is estimated as a function of the two-dimensional Cartesian coordinates (easting and northing). The results of the research led to the conclusion that optimal values of F and P depend on the sample size, i.e., for small data sets F=1.5 and P=7 while for large data sets F=2.5 and P=9. In addition, the accuracy of the interpolated field varies with the sample size. As expected for small sample sizes, the interpolated field and its variability may be significantly underestimated.     

Self-organizing feature map neural network classification of the ASTER data based on wavelet fusion

Remote sensing classification methods can be classified as supervised and unsupervised catalogs. The maximum likelihood method (MLH) is a super-vised classification method,which is widely used in the remote sensing data classification and produces good results[1]. In the MLH, the parameters are esti-mated, assuming that the samples are normally dis-tributed in spectral space, to determine the mean vec-tor and covariance matrix of all classes. In most cases, however, the samples are not norma…     

应用流动单元寻找油田有利挖潜区的新思路

储层流动单元在石油勘探开发领域方面的应用已引起国内外专家学者的广泛关注，但其研究目前主要局限在“静”的方式上，即采用储层静态多参数聚类分析方法研究储集体的宏观非均质单元，而很少考虑油藏实际生产的动态资料，如，注采井网、射孔状况、开采强度、开采措施等。笔者在分析流动单元研究现状的基础上，认为将准确的流动单元划分与定量的剩余油描述有机结合，即“动静”结合，是寻找有利挖潜区的最佳方法，并运用实例说明了该方法在油田生产实践中的应用，对提高油田采收率具有重要的指导意义。     

基于人工神经网络的钢筋混凝土异型节点滞回模型

讨论了应用神经网络技术模拟钢筋混凝土材料滞回行为的若干问题，建立了钢筋混凝土异型节点滞回特性的BP神经网络预测模型，并获得了满意的结果。分析表明，神经网络计算是钢筋混凝土构件抗震性能研究中的一种很有潜力的新方法。     

基于地震-测井数据预测储层参数空间分布规律的神经网络模型

研究了地震资料的特征参数与储层参数间的神经网络模型。建立了相互之间的非线性映射，可以横向预测目的层的储层参数，实现地震资料和测井资料联合预测储层参数的空间分布规律。实验结果表明，该方法是可行的。     

遗传算子对径流预报神经网络模型的影响

采用均匀设计来安排遗传算子组合进行数值实验,研究了遗传算子对神经网络径流预报精度的影响。研究发现,输入模式对最终种群中个体的分布影响不明显,不同算子组合对其影响则明显得多。通过回归分析发现,采用不同算子组合优化神经网络初始权重径流预报精度差别较大,对未归一网络的优化效果较归一网络好,同时采用数据归一输入模式与遗传算法优化神经网络初始权重未产生叠加效果。     

Energy-based probabilistic evaluation of soil liquefaction

This paper presents a seismic wave energy-based method with back-propagation neural networks to assess the liquefaction probability. An empirical equation and Fourier spectrum of acceleration are employed, respectively, to calculate the seismic wave energy. Discriminant analysis is used to determine the equation of the boundary curve separating the data points with and without liquefaction. The proposed method shows capability in evaluating the probability of soil liquefaction based on the boundary curve and a logarithm normal distribution.