基于交会图与模糊聚类算法的复杂岩性识别

针对复杂岩性识别中存在的模糊性,应用交会图和模糊聚类方法对复杂岩性储层的岩性识别问题进行了研究。首先依据岩心取样进行岩性归类,然后通过交会图技术确定了9个对岩性反映较明显的参数,并建立起研究区域的岩性样本集。最后,通过模糊聚类的识别模式,依最大隶属度的原则,建立了岩性判别方法,并应用该方法对海拉尔盆地的岩性进行了识别,取得较好的效果。     

Classification of CBERS-2 Imagery with Fuzzy ARTMAP Classifier

A fuzzy ARTMAP classifier is adopted for a classification experiment of CBERS-2 imagery. The fundamental theory and processing about the algorithm are first introduced, followed with a land-use classification experiment in Shihezi County on CBERS-2 high resolution imagery. Three classifiers are compared: maximum likelihood classifier (MLC), error back propagation (BP) classifier, and fuzzy ARTMAP classifier. The comparison shows comparably better results for the fuzzy ARTMAP classifier, with overall classification accuracy of 9.9% and 4.6% higher than that of MLC and BP. The results also prove that the fuzzy ARTMAP classifier has better discernment in identifying bare soil on CBERS-2 imagery.     

FANN-Based Surface Water Quality Evaluation Model and Its Application in the Shaoguan Area

A fuzzy neural network model is proposed to evaluate water quality. The model contains two parts: first, fuzzy mathematics theory is used to standardize the samples; second, the RBF neural network and ...     

模块化模糊神经网络训练策略研究

为分析模块化模糊神经网络的训练机制,进一步降低其训练复杂度,增强模型的泛化性能,尝试采用部分训练样本对模型进行相对粗训练,并以广西全省1957-2003年连续47 a 5月平均降水量作为实验数据,统计各种参数条件下模型的逐步预测误差情况.结果表明:由部分训练样本参与训练的模型所得测试误差普遍低于由全部训练样本参与训练的模型所得测试误差,证明该方法可行.     

基于标识的遥感影像部分监督模糊聚类

根据遥感影像的特点引入了部分监督模糊聚类的方法,并对其进行改进,加入了标识参数flagk,使其在训练过程中对参与计算的数据进行筛选,使得训练的速度加快,去除了非样本类数据对类心数据的影响,获得了较高精度的分类结果。     

水质评价的污染损害指数公式

在选择了各污染物的“参照值”情况下,用污染物的实测学相对值代替水质污染损害率公式中的浓度监测值,公式中的参数可认为与污染物的特性无关。采用遗传算法优化中的参数,得到了多种水质污染物都能适用的水质污染损害率和污染损害指数公式。提出用对比加权对污染损害分指数进行赋权的新方法,计算水质综合污染损害指数。该方法用于水质综合评价的物理意义明确、计算简单、使用方便,具有普适性和可比性。     

顶点法在边坡稳定性评价中的应用

考虑到边坡稳定性分析中存在的测量数据的不确定性和确定计算参数的复杂性以及描述边坡稳定状态的模糊性, 本文介绍了-种新的适用于边坡稳定性分析的模糊数学方法顶点法, 该法由不确定的土力学参数求得边坡稳定性系数的模糊集合, 然后运用对比法进行分类来确定边坡所处的稳定状态, 文章最后通过-个工程实例阐述了应用过程。     

震后趋势决策支持系统PTDSS

介绍了作者研制的“震后趋势决策支持系统PTDSS”的构成及预报效果,该系统使用模糊联想记忆FAM（Fuzzy Associative Menory)模型将神经网络与模糊系统结合起来,使之具有第2代专家系统的学习功能,系统知识库中除存有专家多年来总结的有关地震现场预报的知识外,还通过系统学习得到了一批十分有意义的,目前专家尚未总结的知识,震例检验的结果该系统有较好的预报效果。     

A Short-Term Climate Prediction Model Based on a Modular Fuzzy Neural Network

In terms of the modular fuzzy neural network (MFNN) combining fuzzy c-mean (FCM) cluster and single-layer neural network, a short-term climate prediction model is developed. It is found from modeling results that the MFNN model for short-term climate prediction has advantages of simple structure, no hidden layer and stable network parameters because of the assembling of sound functions of the selfadaptive learning, association and fuzzy information processing of fuzzy mathematics and neural network methods. The case computational results of Guangxi flood season (JJA) rainfall show that the mean absolute error (MAE) and mean relative error (MRE) of the prediction during 1998 2002 are 68.8 mm and 9.78%, and in comparison with the regression method, under the conditions of the same predictors and period they are 97.8 mm and 12.28% respectively. Furthermore, it is also found from the stability analysis of the modular model that the change of the prediction results of independent samples with training times in the stably convergent interval of the model is less than 1.3 mm. The obvious oscillation phenomenon of prediction results with training times, such as in the common back-propagation neural network (BPNN) model, does not occur, indicating a better practical application potential of the MFNN model.     

城市河流生态系统健康评价初探

健康的河流生态系统已经成为公认的河流管理目标,采用河流生态系统健康理论来研究城市河流问题,具有较强的理论与现实意义。在城市河流生态系统健康概念内涵剖析的基础上,提出了包含水量、水质、水生生物、物理结构与河岸带5大要素的指标体系及其"很健康、健康、亚健康、不健康、病态"5级评价标准,建立了模糊层次综合评价程序与模型。并以宁波市河流为例进行实证研究,明确各河流的健康状况及限制因子,为其保护、维育与修复提供了决策依据。