基于灰色预测模型的概率积分法参数确定研究

运用灰色预测模型可以对矿山开采引起的地表下沉和水平移动的观测数据进行处理与分析,进而确定概率积分法参数.根据潞安矿区司马矿地表移动观测站的实测资料,通过建立灰色预测GM(1,1)模型,利用Matlab对观测数据进行插值处理,获得等间隔数据,求取了概率积分法参数,并进行了相关精度分析.在此基础上,将灰色系统预测方法与曲线拟合方法求取参数的结果进行了对比分析,指出了两种方法存在误差的影响因素.结果表明,运用灰色系统理论求取的概率积分法的参数可靠,体现了动态预测的特点.同时,提出可以通过实施定期等间隔的观测和加大对衰退期的观测次数等措施,提高概率积分法参数的精度.     

水平煤层半无限开采时沉陷参数误差对下沉预计结果精度的影响

本文依据误差理论，系统地分析了水平煤层半无限开采时概率积分法、威布尔分布法和样条概率积分法的参数误差对下沉预计结果精度的影响，弄清了预计结果精度的分布规律，评述了它们的优缺点，并指出了改进措施。本文的研究结果对开采沉陷观测站设计、开采沉陷参数及计算模型的识别具有指导意义。     

基于模矢法的概率积分预计参数解算

为了获取准确的沉陷区概率积分预计参数,提出一种基于模矢法的概率积分参数反演方法。该方法以地表沉陷预计模型(概率积分法)为例,建立概率积分参数与地表沉陷的定量关系模型,提出基于模矢法的概率积分参数反演方法,并编制应用程序。朱集煤矿1222工作面工程应用表明:走向线方向上的下沉中误差在±20.85mm之间;沿倾向线方向上的下沉中误差在±14.69mm之间,反演的预计值与实测值大致吻合;得出通过模矢法求解的概率积分法预计参数符合淮南矿区地下采空区沉陷的基本规律。     

村庄下条带开采对地表建筑的影响预测及评价

为实现在村庄建筑物不搬迁的情况下安全采煤、提高资源采出率、缓解矿井生产接替紧张的矛盾,结合矿区具体的地质采矿条件及地表村庄建筑物的现状,给出了条带开采设计方案．根据万年矿已有的地表移动观测资料,该矿区地表沉陷规律基本符合概率积分法．通过理论分析计算,确定了概率积分法的预计参数,并对地表移动和变形进行了预计,根据预计结果对村庄建筑物损害程度进行了分析和评价．研究结果表明,万年矿村庄下压煤采用条带法开采技术上可行,经济上合理．     

时序分析在开采沉陷动态参数预计中的应用

应用时间序列分析法,对开采沉陷动态过程的概率积分法预计参数进行分析,建立动态预计模型。用该模型可对参数的未来值进行预计,然后利用预计结果进一步预计地表的移动变形,解决了开采沉陷的动态预计问题。应用此法,地表下沉的相对预计误差一般为４％左右,与传统方法相比,预计精度可提高５％～１５％。     

巨厚松散层下条带开采地表移动规律分析

巨厚松散层下条带开采引起的地表移动规律及地表移动参数有明显的特点,采用概率积分法预计地表移动变形结果的精确与否,关键在于地表移动参数．针对焦作矿区内巨厚松散层的地层条件,以条带开采地表移动实测资料为基础,用数学方法分析了巨厚松散层下条带开采地表移动规律及地表移动参数,并得出地表移动参数与地质采矿条件之间的函数式．     

基于简化Bishop积分法降雨滑坡失效概率模型研究

摘要：为了研究降雨对降雨型滑坡的影响,利用不同降雨重现期的前期有效降雨量,分析不同降雨入渗条件下的滑坡土体含水率的变化对土体抗剪强度参数和基质吸力的影响,基于简化Bishop积分法和响应面法的基本原理,用积分法代替有限条分求和,考虑土体强度参数的变异性及降雨自身随机性,提出了一种新的降雨滑坡失效概率模型,定量评价不同降雨条件下的滑坡失效概率并采用该方法对某一实际滑坡进行研究分析。计算结果表明,随着降雨重现期与降雨天数的增加,滑坡的失效概率越高,但考虑降雨自身的概率时,滑坡的失效概率并不是随着降雨重现期的增加而增加,且考虑基质吸力所得的稳定系数略高,失效概率稍低。     

用优化参数降低芳烃抽提过程中丁烷的损耗量

针对如何降低芳烃抽取过程中的丁烷消耗问题，提出优化过程操作参数的解决方法．其中优化问题的模型用人工神经网络来描述，优化方法采用遗传算法．介绍了人工神经网络建模的过程和遗传算法寻优的步骤．     

基于人工神经网络的电力电子电路故障诊断

提出了采用基于波形直接分析的人工神经网络故障诊断方法实现电力电子电路的故障诊断．以三相整流电路的诊断为例，设计了人工神经网络的层数和点数，建立了三相整流电路的模型获取了样本数据，并训练了人工神经网络．仿真表明用四层人工神经网络对三相整流电路进行诊断可获得正确结果、     

基于Origin的深部开采条件下地表移动预测研究

随着我国煤炭资源逐渐转向深部开采的发展趋势,开采沉陷带来了深部岩体变形问题和地表沉陷预测等技术难题.为了能更好地预测地表沉陷与深部岩石力学性质变化的关系,采用Origin曲线拟合函数中的非线性最小平方拟合来获得预测函数概率积分法参数,然后利用拟合出来的参数去分析和探讨深部开采地表沉陷规律,同时回归分析了所选预测函数模型及其效果.     

SVM model for estimating the parameters of the probability-integral method of predicting mining subsidence

A new mathematical model to estimate the parameters of the probability-integral method for mining subsidence prediction is proposed. Based on least squares support vector machine (LS-SVM) theory, it is capable of improving the precision and reliability of mining subsidence prediction. Many of the geological and mining factors involved are related in a nonlinear way. The new model is based on statistical theory (SLT) and empirical risk minimization (ERM) principles. Typical data collected from observation stations were used for the learning and training samples. The calculated results from the LS-SVM model were compared with the prediction results of a back propagation neural network (BPNN) model. The results show that the parameters were more precisely predicted by the LS-SVM model than by the BPNN model. The LS-SVM model was faster in computation and had better generalized performance. It provides a highly effective method for calculating the predicting parameters of the probability-integral method.     

离岸混凝土氯离子扩散系数的人工神经网络模型

为研究人工神经网络在离岸混凝土氯离子渗透中的应用,从已有文献中选用653组数据,建立网络结构为13-27-1的模型进行训练、预测。研究结果表明:人工神经网络能有效预测离岸混凝土中的氯离子扩散系数;水灰比,水泥、减水剂、外加剂(粉煤灰、矿渣、硅灰)、骨料的含量以及混凝土的抗压强度、养护机制、试验方法、暴露时间和暴露环境均会对氯离子扩散系数产生影响。     

Combined ANN prediction model for failure depth of coal seam floors

Failure depth of coal seam floors is one of the important considerations that must be kept in mind when mining is carried out above a confined aquifer. In order to study the factors that affect the failure depth of coal seam floors such as mining depth, coal seam pitch, mining thickness, workface length and faults, we propose a combined artificial neural networks (ANN) prediction model for failure depth of coal seam floors on the basis of existing engineering data by using genetic algorithms to train the ANN. A practical engineering application at the Taoyuan Coal Mine indicates that this method can effectively determine the network struc-ture and training parameters, with the predicted results agreeing with practical measurements. Therefore, this method can be applied to relevant engineering projects with satisfactory results.     

Simulation of the Ultrasonic Precipitation Process of Nano-hydroxyapatite by an Artificial Neural Network

1Introduction Hydroxyapatite(HA)hasbeenwidelyconsideredas oneofthemostimportantbioceramicsformedicalandden talapplicationssuchasdentalimplants,alveolarbridgeaugmentation,orthopaedics,maxillofacialsurgeryanddrug deliverysystemsduetoitsbiocompatibility,andc…     

利用BP神经网络描述作物-水模型

研究了利用BP神经网络描述作物－水模型的方法，将各生育阶段的耗水量作为输入，将作物单产作出输出，构建了具有1个隐含单元层的BP神经网络结构，利用北京永乐店试验站1998－1999年度的冬小麦灌溉试验资料进行了样本进行训练和检验，得到了作物－水模型的人工神经网络模型。结果表明，作物－水模型的人工神经网络模型具有较高的拟合精度和预测精度。     

Modeling of CVI process in fabrication of carbon/carbon composites by an artificial neural network

C/C composites maintain unique intensity features at temperatures as high as 2600℃. Be-cause of their superior thermal and mechanical properties, which can persist at ultra-high tem-peratures, C/C composites are used in many areas including national defense, aviation aerospace, etc. At present, high performance C/C composites are mainly fabricated by using CVI proc-esses[1,2]. But it should be pointed out that the CVI process is controlled by many factors such as the infiltration temperat…     

Research on Mineral Resource Prediction by GA-ANN

1　 IntroductionThe research objectives of mineral engineer-ing are usually large systems. Compared withprocessing industry,the mineral industry has itsown features,i.e.the operation objective is natu-ral occurrence of mineral and rock,the operationsite continually moves,and mineral production isa complex process composed of multiple links andprocedures.Therefore,there are two points thatshould be emphasized in the study on mineral en-gineering optimization[1] :1 ) The research on temporary an…     

用于有机化学品生物活性预测的人工神经网络

对人工神经网络用于化学品生物活性预测中遇到的技术问题进行了系统的研究,提出了防止网络训练过拟合及局部最小的方法,对影响网络收敛的各技术参数的选择进行了探讨。确定的人工神经网络计算软件用于实际有机化学品生物活性预测,效果优于文献报道结果。     

Optimization of thermomechanical processes in Cu-Cr-Zr lead frame alloy using neural networks and genetic algorithms

1 Introduction Copper alloys are the most popular lead frame alloys and are used in plastic packag-ing application due to high thermal and electrical conductivity as well as high strength[1-3]. The thermomechanical treatment technology is one of the most used methods to attain high mechcanical and electrical properties of the copper alloys[4-6]. The thermomechanical processes have been mainly studied empirically with the trial-and-error method so far. There are few reports in the literature de…     

Artificial Neural Network Performing the Forward Operation of Color Appearance Model

A method of the forward operation of color appearance(from colorimetric attributes to color appearanceattributes)using an artificial neural network(ANN)is presented.The neural network model developed is a multilayerfeedforward neural network model for predicting color appearance model(CAM).This method greatly decreased themathematical computation in color appearance prediction.The error backed-propagation(BP)algorithm was applied