变结构人工神经网络模型及其在成矿预测中的应用

针对人工神经网络成矿预测模型结构难以确定的问题，详细阐述了一种在模型训练中进行隐层数目及隐层单元数目动态调整的人工神经网络算法，并以VC 为开发工具实现了变结构人工神经网络成矿预测模型，经用华南26个岩体检验，回忆率及预测率均高达100％。该方法提供了一种面向具体问题的动态解决方案，在成矿预测工作中具有一定的实用性。     

基于GIS的人工神经网络模型在铀成矿预测中的应用——以塔里木盆地北缘为例

利用地理信息系统（GIS）与人工神经网络（ANN）相结合,研究设计了基于GIS的人工神经网络成矿预测系统,实现了从地质变量优选到人工神经网络成矿预测结果图形显示的计算机自动化处理。在塔里木盆地北缘铀成矿预测中,应用效果良好。     

基于GIS的人工神经网络矿产预测系统设计

将地理信息系统(GIS)和人工神经网络(ANN)相结合,提出基于GIS的人工神经网络矿产预测系统设计方案,实现了从地质变量优选到人工神经网络成矿预测结果图形显示的计算机自动化处理,并对其中的主要模块:地质变量优选模块、数据库管理模块和神经网络成矿预测模块进行了介绍,简要说明了各模块功能实现中所应用的主要技术.     

煤矿立井井筒非采动破裂的人工神经网络预测

应用人工神经网络的基本原理,建立了一个基于神经网络的煤矿立井井筒非采动破裂的预测系统,实现了立井井筒破裂预测的智能化。最后将神经网络预测结果与数值计算结果对比,认为应用人工神经网络对立井井筒破裂时间的预测比较准确、实用。     

基于GIS与ANN耦合技术的非线性矿产预测系统设计

利用地理信息系统(GIS)和人工神经网络(ANN)相结合,提出的基于GIS的人工神经网络矿产预测系统设计方案,实现了从地质变量优选到人工神经网络成矿预测结果图形显示的计算机自动化处理。其中的主要模块为:地质变量优选模块、数据库管理模块、神经网络成矿预测模块和图形处理功能模块。它们之间通过数据库进行数据传输与共享达到互相连接。     

基于人工神经网络的岩爆预测方法

在分析岩爆主要影响因素的基础上，建立了一种新的人工神经网络岩爆预测模型。采用已有岩爆发生数据作为训练样本对网络进行训练，利用收敛的网络进行岩爆烈度预测，预测结果与实际吻合，说明利用人工神经网络预测岩爆发生烈度是一种可行的方法。     

神经网络分析方法在瓦斯预测中的应用

论述了瓦斯预测技术的研究现状及其在现代矿业中面临的新问题，介绍了神经网络技术在处理复杂地质条件方面的优越性，探讨了瓦斯预测技术与人工神经网络等高新技术相结合的可能性与必要性，并举例论证了它们在瓦斯预测过程中的适用性。实践证明：瓦斯预测技术与人工神经网络相结合所建立的预测模型，不仅能够综合考虑各种影 响因素，较好地处理地质条件中的各种非线性关系，而且预测精度高，结论可靠，为瓦斯预测技术的进一步发展提供了新的思路。     

人工神经网络方法在基坑变形预测中的应用研究

分析研究了人工神经网络方法在基坑变形预测中的建模方法,并通过实例应用,证明这种方法是切实可行的.同时将人工神经网络方法预测结果和灰色系统模型及时序模型预测进行比较,充分证明人工神经网络方法在变形预测中的优越性.     

人工神经网络在实时卡钻预测中的应用研究

采用人工神经网络模型中BP模型对现场的钻井工况进行卡钻预测 ,并开发了人工神经网络实时卡钻预测系统软件。     

岩溶煤矿矿井煤层底板突水非线性预测方法研究

本文煤层底板突水系统为一非线性系统的特性,提出采用对非线性问题具有良好适用性的人工神经网络方法进行煤层底板突水预测。文中系统地阐述了人工神经网络预测的原理、建模方法、适用条件和应用问题,并在肥城矿务局对所建立的煤层底板突水预测人工神经网络系统进行生产性检验,取得良好的效果,说明人工神经网络方法应用于煤层底板突水预测的可靠性。     

Uncertainty analysis in bed load transport prediction of gravel bed rivers by ANN and ANFIS

River flow is a complex dynamic system of hydraulic and sediment transport. Bed load transport have a dynamic nature in gravel bed rivers and because of the complexity of the phenomenon include uncertainties in predictions. In the present paper, two methods based on the Artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are developed by using 360 data points. Totally, 21 different combination of input parameters are used for predicting bed load transport in gravel bed rivers. In order to acquire reliable data subsets of training and testing, subset selection of maximum dissimilarity (SSMD) method, rather than classical trial and error method, is used in finding randomly manipulation of these subsets. Furthermore, uncertainty analysis of ANN and ANFIS models are determined using Monte Carlo simulation. Two uncertainty indices of d factor and 95% prediction uncertainty and uncertainty bounds in comparison with observed values show that these models have relatively large uncertainties in bed load predictions and using of them in practical problems requires considerable effort on training and developing processes. Results indicated that ANFIS and ANN are suitable models for predicting bed load transport; but there are many uncertainties in determination of bed load transport by ANFIS and ANN, especially for high sediment loads. Based on the predictions and confidence intervals, the superiority of ANFIS to those of ANN is proved.     

基于模糊小波神经网络的岩溶隧道风险评估及综合超前地质预报技术

针对岩溶隧道突水风险评估的不确定性和复杂性以及传统的数学方法在评估安全风险等级中的局限性,将人工神经网络理论、小波分析及模糊评价法有机结合,建立了基于模糊小波神经网络的岩溶突水安全风险评估模型。根据各种物探方法的优缺点和对岩溶水预报的敏感性,结合综合超前地质预报方法和原则,提出地质分析、风险等级划分、分级综合预报及施工地质灾害临近警报技术相结合的综合地质预报方案。通过在齐岳山岩溶隧道实施,成功预报了隧道掌子面前方的岩溶水,证实了该方案的科学性和可行性。     

人工神经网络在爆破块度预测中的应用研究

利用人工神经网络模型对爆破块度进行预测,实验结果表明,该方法是完全可行的。通过对实验样本数据进行归一化处理后再对人工神经网络模型进行训练和预测,其预测精度会得到大大提高。     

岩溶塌陷不确定性预测评价综述

文章梳理了岩溶塌陷不确定性预测评价的发展历程和最新进展,总结形成了预测评价的一般思路,认为评价时应立足于评价区的地质背景和实际塌陷情况,遵守“从定性到定量”的原则,按照评价区实际情况,依次完成影响因素分析、评价因子选取、评价模型构建3个步骤,并对如何完成好这3个步骤进行了详细阐述;还针对常用的模糊综合评价模型、灰色模糊综合评判模型、信息量模型、证据权模型、人工神经网络模型、支持向量机模型,在特点、核心步骤、优势、弊端4个方面进行了分类对比分析。文章认为目前国内的岩溶塌陷不确定性预测评价还存在较大滞后,仍有许多问题亟待解决。      

基于ANN与GIS技术的区域岩溶塌陷稳定性预测———以桂林西城区为例

岩溶地面塌陷是岩溶区常见的一种地质灾害, 塌陷区域预测是进行国土规划、资源开发与灾害防治的必要工作.由于岩溶塌陷的影响因素众多且相互作用, 发展过程复杂, 加之各评价因子的数值获取困难, 致使长期以来塌陷区域定量预测成为一个难以解决的课题.现行的区域预测模型不能描述塌陷形成模式的非线性特征, 也难以克服评价因子权重确定过程中人为经验因素的影响.神经网络技术的自学习、自适应与高度非线性映射特点显示了其在塌陷区域预测领域中应用的前景.根据研究区内地面塌陷空间聚集分布的特征, 提出了不同因子组合条件下塌陷发生可能性的定量化方法, 结合选定的评价因子类别确定了神经网络预测模型的结构, 利用312个塌陷点样本中的292个进行网络训练, 余下的20个样本的校验结果表明该模型具有较高的可信度.运用GIS技术将研究区进行评价单元划分, 并获取各评价因子的取值, 输入到训练好的网络中进行预测.将各单元的输出值进行归并处理后得到研究区岩溶塌陷的稳定级分区图.      

Aquifer hydraulic conductivity prediction via coupling model of MCMC-ANN

Grain-size distribution data,as a substitute for measuring hydraulic conductivity(K),has often been used to get K value indirectly.With grain-size distribution data of 150 sets of samples being input data,this study combined the Artificial Neural Network technology(ANN)and Markov Chain Monte Carlo method(MCMC),which replaced the Monte Carlo method(MC)of Generalized Likelihood Uncertainty Estimation(GLUE),to establish the GLUE-ANN model for hydraulic conductivity prediction and uncertainty analysis.By means of applying the GLUE-ANN model to a typical piedmont region and central region of North China Plain,and being compared with actually measured values of hydraulic conductivity,the relative error ranges are between 1.55%and 23.53%and between 14.08%and 27.22%respectively,the accuracy of which can meet the requirements of groundwater resources assessment.The global best parameter gained through posterior distribution test indicates that the GLUEANN model,which has satisfying sampling efficiency and optimization capability,is able to reasonably reflect the uncertainty of hydrogeological parameters.Furthermore,the influence of stochastic observation error(SOE)in grain-size analysis upon prediction of hydraulic conductivity was discussed,and it is believed that the influence can not be neglected.     

FORECAST OF PREFERRED FAULT BASED ON NEURAL NETWORK

基于优势面区域稳定性评价理论和人工神经网络 (ANN)的原理和方法 ,探讨了基于 ANN的优势断裂预报神经网络算法及模型 ,并结合实例检验表明应用反传 (BP)神经网络模型判定优势断裂的新方法是有效的 ,且取得了理想的结果。     

An evaluation of existent methods for estimation of embankment dam breach parameters

The study of dam-break analysis is considered important to predict the peak discharge during dam failure. This is essential to assess economic, social and environmental impacts downstream and to prepare the emergency response plan. Dam breach parameters such as breach width, breach height and breach formation time are the key variables to estimate the peak discharge during dam break. This study presents the evaluation of existing methods for estimation of dam breach parameters. Since all of these methods adopt regression analysis, uncertainty analysis of these methods becomes necessary to assess their performance. Uncertainty was performed using the data of more than 140 case studies of past recorded failures of dams, collected from different sources in the literature. The accuracy of the existing methods was tested, and the values of mean absolute relative error were found to be ranging from 0.39 to 1.05 for dam breach width estimation and from 0.6 to 0.8 for dam failure time estimation. In this study, artificial neural network (ANN) was recommended as an alternate method for estimation of dam breach parameters. The ANN method is proposed due to its accurate prediction when it was applied to similar other cases in water resources.

     

New approaches to determine the ultimate bearing capacity of shallow foundations based on artificial neural networks and ant colony optimization

In this study, two different approaches are proposed to determine the ultimate bearing capacity of shallow foundations on granular soil. Firstly, an artificial neural network (ANN) model is proposed to predict the ultimate bearing capacity. The performance of the proposed neural model is compared with results of the Adaptive Neuro Fuzzy Inference System, Fuzzy Inference System and ANN, which are taken in literature. It is clearly seen that the performance of the ANN model in our study is better than that of the other prediction methods. Secondly, an improved Meyerhof formula is proposed for the computation of the ultimate bearing capacity by using a parallel ant colony optimization algorithm. The results achieved from the proposed formula are compared with those obtained from the Meyerhof, Hansen and Vesic computation formulas. Simulation results showed that the improved Meyerhof formula gave more accurate results than the other theoretical computation formulas. In conclusion, the improved Meyerhof formula could be successfully used for calculating the ultimate bearing capacity of shallow foundations.     

土工合成材料耐久性的神经网络预测

以BP人工神经网络为工具,利用其强大的非线性映射能力,在综合分析土工合成材料耐久性影响因素的基础上将土工合成材料所处环境的温度、湿度、紫外线照射情况以及土工合成材料的老化时间作为网络的输入参数,以描述土工合成材料耐久性状态的强度和延伸率作为网络的输出,建立了神经网络模型。采用大量试验数据对网络进行了训练和检验,并对土工合成材料的耐久性进行了预测。结果表明,预测值和试验结果比较接近,该网络能较好地反映土工合成材料耐久性与其影响因素之间的非线性映射关系。