基于区间树索引的等高线提取算法

重新设计了从高程格网中提取等高线过程中的遍历策略，以保证提取结果具有统一的方向；针对日益增长的高程格网数据量，提出了基于区间树索引来查找等高线起点的算法。     

基于决策树的高光谱数据特征选择及其对分类结果的影响分析

本文利用OM IS高光谱数据,研究了决策树算法（Decision Tree,DT）特征选择的特点以及特征选择对决策树分类结果的影响。设计了三种特征选择方法：SEP,MDLM和RELIEF,将它们与DT特征选择的结果以及特征选择后的分类精度（考虑了三种分类器：最大似然法、后向传播神经网络、最邻近法）进行对比,并分析了这三种特征选择方法对决策树结构和分类精度的影响。结果显示,DT是一种比较好的特征选择方法;经过特征选择后再生成的决策树比直接生成的决策树,用到更少的特征（平均减少了43.36%）、有更多的节点（平均增加了18.61%）和更高的分类精度（平均提高了0.35%）,当样本数量少时,分类精度的提高幅度最大,而树的大小却基本没有增加。     

参数调整对气象预测的B-P网络模型性能的影响

为了探索ＢＰ网络的参数调整特性,进行了参数α、β的选取对ＢＰ算法的收敛速度和模型的稳定性的影响研究。通过ＢＰ网络用于气象预测建模的参数调整个例分析表明：参数α、β的取值对ＢＰ模型的稳定性无显著影响,但参数值的调整尤其是β值的调整对建模的收敛速度有明显的影响。     

基于神经网络的滑坡变形预测报研究

利用ＢＰ神经网络方法建立了滑坡变形预报模型,在此模型的基础上对几个典型滑坡进行了预报分析,其结果表明用ＢＰ模型进行滑坡短期预报效果较好。     

河北省环渤海地区生态经济协调发展的人工神经网络分析

区域社会经济系统的可持续发展依赖于系统成分的协调程度及与生态系统的增益耦合。传统研究多以系统动力学或多目标规划模型来调控系统。文中以人工神经网络模型来模拟和映射社会经济系统的结构和功能。通过构建、训练、测试、运行河北省环渤海地区生态经济网络模型,提出了以减缓发展速度、降低能量投入水平等措施来达到生态系统和社会经济系统协调发展的目标。     

储层裂缝特征测井解释方法综述

为了更好地研究裂缝性砂砾岩储层和其它裂缝性储层裂缝的测井解释问题,综述了目前在裂缝性储层裂缝测井解释中主要采用的常规评价、人工神经网络和斯通利波３类方法在识别裂缝带和定量计算储层裂缝参数两个方面的应用原理和研究现状,并指出了今后的一些研究方向。     

陕西省人工神经元网络降水年,季度预报系统

利用Ｂ－Ｐ人工神经元网络进行了陕西省年度,季度降水预报试验,提出了利用０－１模型解决多等级预报问题的方法,并建立了年度,季度等级预报模型,经过试验,表明该方法预报效果良好,最后对模式在应用中的一些问题及目前其它预报模型的差异等进行了讨论。     

A systematic approach to the input determination for neural network rainfall–runoff models

Input determination has a great influence on the performance of artificial neural network (ANN) rainfall–runoff models. To improve the performance of ANN models, a systematic approach to the input determination for ANN models is proposed. In the proposed approach, the irrelevant inputs are removed. Then an adequate ANN model, which only includes highly relevant inputs, is constructed. Unlike the trial‐and‐error procedure, the proposed approach is more systematic and avoids unnecessary trials. To demonstrate the effectiveness of the proposed approach, an application to actual typhoon events is presented. The results show that the proposed ANN model, which is constructed by the proposed approach, has advantages over those obtained by the trial‐and‐error procedure. The proposed ANN model has a simpler architecture, needs less training time, and performs better. The proposed ANN model is recommended as an alternative to existing rainfall–runoff ANN models. Copyright © 2007 John Wiley & Sons, Ltd.     

Prediction of the sawing quality of Marmarit stones using the capability of artificial neural network

The sawing rate is one of the most significant and effective parameters in extracting building stones via diamond wire sawing. This parameter designates the capability of diamond wire sawing for sawing different stones; in addition, the parameter gives rise to economical considerations for quarry designers. In this study, the existent relations between stone geotechnical parameters and the sawing rate of stones via diamond wire sawing were analyzed using regression and correlation coefficient as well as the collected data from Marmarit stone quarries. Moreover, we estimated the sawing rate of Marmarit using the dimensional stone rock mass rating (DSRMR); upon comparison of the data obtained from DSRMR our pre‐collected data on quarries, we did not gain satisfactory results from DSRMR, hence we used artificial neural network (ANN). The results showed that the percentage of Silica, the coefficient of water absorption, the uniaxial compressive strength (UCS), and abrasive hardness are the proper parameters for creating the ANN. Discontinuities have the least effects possible on diamond wire sawing. Having given the training possibility of the ANN, and its ability to evaluate relations among input parameters, the ANN, which was being trained with Marmarit's traits, was an accurate network for estimating diamond wire sawing in Marmarit quarries, although it could not generalize this network for other stones such as Chini and Crystal. Copyright © 2011 John Wiley & Sons, Ltd.     

Application of integrated back‐propagation network and self organizing map for flood forecasting

Much of the nonlinearity and uncertainty regarding the flood process is because hydrologic data required for estimation are often tremendously difficult to obtain. This study employed a back‐propagation network (BPN) as the main structure in flood forecasting to learn and to demonstrate the sophisticated nonlinear mapping relationship. However, a deterministic BPN model implies high uncertainty and poor consistency for verification work even when the learning performance is satisfactory for flood forecasting. Therefore, a novel procedure was proposed in this investigation which integrates linear transfer function (LTF) and self‐organizing map (SOM) to efficiently determine the intervals of weights and biases of a flood forecasting neural network to avoid the above problems. A SOM network with classification ability was applied to the solutions and parameters of the BPN model in the learning stage, to classify the network parameter rules and to obtain the winning parameters. The outcomes from the previous stage were then used as the ranges of the parameters in the recall stage. Finally, a case study was carried out in Wu‐Shi basin to demonstrate the effectiveness of the proposal. Copyright © 2009 John Wiley & Sons, Ltd.