基于PCMRA-SVM的采煤工作面瓦斯涌出量预测

为了准确、快速地预测采煤工作面瓦斯涌出量,针对瓦斯涌出系统的特点,提出了一种基于PCMRA-SVM的瓦斯涌出量预测模型。以钱营矿的25组瓦斯涌出量观测数据进行仿真实验,并与BPNN、SVM、CIGOA-ENN方法的预测结果进行对比。结果表明:PCMRA-SVM模型的最大、最小和平均相对误差分别为4.06%、0.02%和1.73%,均优于CIGOA-ENN、SVM、BPNN,验证了所提出模型的有效性、可靠性及准确性。     

基于PCA-ELM的矿井瓦斯涌出量预测

为进一步提高矿井瓦斯涌出量的预测效率和精度,将主成分分析法(PCA)和极限学习机(ELM)神经网络相结合,建立基于PCA-ELM的矿井瓦斯涌出量预测模型。运用主成分分析法对矿井瓦斯涌出量影响因素样本进行主成分提取,去除各变量之间的线性相关,得到降维后的有效因子。再将这些有效因子作为ELM神经网络的输入层进行训练和预测,借助ELM神经网络不需较多参数调整、学习速度快、泛化性能好的特点,进行快速准确的预测。利用某典型矿井的实测数据进行实例分析,PCA-ELM方法预测的最大误差为0.2589,最小误差为0.0312,平均误差为0.1370,结果表明该预测模型预测速度快、精度高,能够用于矿井瓦斯涌出量预测。     

六家煤矿WIIN1 6-7综放工作面瓦斯异常涌出综合治理研究

针对六家煤矿WIIN1 6-7综放工作面瓦斯涌出异常的现象，应用分源预测法预测了本工作面的瓦斯涌出量，得出其主要来源于上部顶板采空区；通过地面大气压力与井下瓦斯浓度的实测，分析其原因是由于地面大气压的变化。针对此特点采取了上部采空区钻孔抽放和本煤层采空区埋管抽放相结合的治理措施，并增加了注氮措施和采用三通和活塞的方法安装管路，取得了行之有效的效果。     

基于RS GA ELM的矿井瓦斯涌出量预测

为了对煤矿井下瓦斯涌出量进行预测,采用粗糙集与改进极限学习机相结合的方法,在样本数据的筛选上吸取粗糙集数据约简的优点,充分利用极限学习机训练速度快、具有良好泛化性能的特点,并结合遗传算法选择最优的输入权值矩阵和隐含层偏差,避免随机产生所造成的误差。利用编写程序确定隐含层神经元个数,比依靠经验更为准确。在实际应用中选取煤层瓦斯含量、煤层埋藏深度、煤层厚度、煤层间距、工作面日产量五个因素作为预测的影响参数。研究结果表明:该预测模型预测的最大相对误差为5687 1%,最小相对误差为0,平均相对误差为2582 7%,相比改进前的预测模型具有更强的泛化能力和更高的预测精度。     

掘进工作面瓦斯涌出规律研究

通过对潘一矿掘进工作面涌出量进行实测,对工作面瓦斯涌出来源及构成进行了分析。利用回归分析法,找出了工作面落煤、煤壁瓦斯涌出规律,为工作面的通风管理与安全生产提供了必要的技术指导。     

基于PCA-GA-BP的回采工作面瓦斯涌出量预测模型研究

为进一步提高回采工作面瓦斯涌出量预测的准确性,建立了主成分分析法(PCA)、遗传算法(GA)、BP神经网络相结合的预测模型。该模型采用主成分分析法降维处理原始输入数据;将主成分分析结果作为BP神经网络的输入,消除冗余信息;然后采用遗传算法优化BP神经网络的初始权值和阈值,有效克服BP神经网络极易陷入局部最优的问题。选取某矿井回采工作面的实测数据进行分析,结果表明,该模型较单一BP神经网络预测精度高,能更有效地实现回采工作面瓦斯涌出量的高准确度预测。     

基于主成分回归分析的瓦斯含量预测

为了增加多元回归模型预测的精度,将主成分分析与多元回归分析相结合提出了PCA—MRA模型,并将该模型用于实际瓦斯含量预测。结果表明,PCA—MRA模型消除了输入变量之间的相关性,减少了输入变量值个数,提高了预测精度,便于实际推广和应用,为瓦斯含量预测提供一种新的途径。     

基于K-CV&SVM的工作面煤层瓦斯含量预测

为进一步提高工作面煤层瓦斯含量预测的准确性,将交叉验证方法(K-CV)和支持向量机(SVM)相结合,建立预测模型。该模型在SVM的基础上采用交叉验证的思想,寻找最佳参数cg,最大限度地消除由于个别样本的较大误差对预测模型的影响,提高预测模型的准确性。选取告成矿工作面煤层钻孔的实测数据进行实例分析,结果表明:该模型较单一SVM预测精度高,能有效预测工作面煤层瓦斯含量。     

改进果蝇优化算法优化广义回归神经网络的预测问题研究

为提高传统非线性预测模型的预测精度,提出一种基于改进果蝇优化算法优化广义回归神经网络的预测方法,将果蝇群体分两部分分别进行迭代寻优,从而改进了果蝇优化算法的寻优性能,进而避免了在寻优过程中陷入局部最优。该方法利用改进果蝇优化算法优化广义回归神经网络的径向基函数扩展参数,然后用训练好的广义回归神经网络预测模型进行预测,最后通过订单预测算例进行实证研究。实证研究结果显示,该方法在解决订单预测问题中与未改进的果蝇优化算法优化广义回归神经网络和传统的广义回归神经网络方法对比,具有更高的预测精度和更好的非线性拟合能力。     

采空区瓦斯源位置对高位抽采的影响及优化措施研究

为提高高抽巷对采空区下部涌出瓦斯的治理效果,基于流体动力学、传质传热学理论,结合采场覆岩裂隙演化规律,构建三维多场耦合解算模型。运用Fluent软件分别模拟了采空区顶板、底板及整体涌出时的瓦斯运移积聚特征,并对高抽巷抽采和上隅角瓦斯浓度进行了量化对比分析。结果表明,顶板瓦斯涌出时,高浓度瓦斯积聚于采空区上部,主要靠分子扩散和机械弥散与下部漏风混合;采空区整体及底板瓦斯涌出时,"瓦斯-风流"的主要混合方式向对流扩散转变,采空区上部积聚瓦斯浓度降低,更多瓦斯随下部漏风涌向上隅角,高抽巷抽采浓度和上隅角瓦斯控制能力下降;此时,通过隅角封堵控制漏风,能够大幅提高抽采效率,在较低抽采流量条件下实现上隅角瓦斯治理。     

Prediction and accommodation revisited

The paper presents a further articulation and defence of the view on prediction and accommodation that I have proposed earlier. It operates by analysing two accounts of the issue—by Patrick Maher and by Marc Lange—that, at least at first sight, appear to be rivals to my own. Maher claims that the time-order of theory and evidence may be important in terms of degree of confirmation, while that claim is explicitly denied in my account. I argue, however, that when his account is analysed, Maher reveals no scientifically significant way in which the time-order counts, and that indeed his view is in the end best regarded as a less than optimally formulated version of my own. Lange has also responded to Maher by arguing that the apparent relevance of temporal considerations is merely apparent: what is really involved, according to Lange, is whether or not a hypothesis constitutes an “arbitrary conjunction.” I argue that Lange’s suggestion fails: the correct analysis of his and Maher’s examples is that provided by my account.     

Prediction and explanation of chemical mutation sites

Résumé En ce qui concerne I'ARN du VMT, les codons voisins y présentent des arrangements analogues. Il s'agit seulement des codons qui subissent une mutation sous l'action de l'acide nitreux, mutation qui conduit à une modification de la protéine de l'enveloppe. Cette observation suggère, que l'examen minutieux de la séquence d'un acide nucléique pourrait permettre de prédire les sites probables où aura lieu la mutation chimique et que les «hot spots» pour une mutation spontanée pourraient aussi avoir des codons voisins caractéristiques.     

短期气候数值预测的进展和发展前景

本文评述了目前短期气候数值预测方面存在的缺陷,提出需要解决的四个挑战性的问题。认为在短期气候数值预测领域应结合我国学者自己已有的科研成果和经验,在计算条件相对落后的情况下,走一条具有我国自己特色的创新之路,而这可能是解决短期气候数值预测目前所存在的缺陷的一条行之有效的途径。     

Prediction in a duration model

Predictors of mean duration and an arbitrary quantile are given for the Weibull regression model for duration data. Associated prediction variances arising from maximum likelihood and least squares estimation are given. In an empirical example, based on duration of employment data, the uses of various model diagnostics and the predictors are illustrated.     

Prediction,diagnosis, and causal thinking in forecasting

While forecasting involves forward/predictive thinking, it depends crucially on prior diagnosis for suggesting a model of the phenomenon, for defining‘relevant’variables, and for evaluating forecast accuracy via the model. The nature of diagnostic thinking is examined with respect to these activities. We first consider the difficulties of evaluating forecast accuracy without a causal model of what generates outcomes. We then discuss the development of models by considering how attention is directed to variables via analogy and metaphor as well as by what is unusual or abnormal. The causal relevance of variables is then assessed by reference to probabilistic signs called‘cues to causality’. These are: temporal order, constant conjunction, contiguity in time and space, number of alternative explanations, similarity, predictive validity, and robustness. The probabilistic nature of the cues is emphasized by discussing the concept of spurious correlation and how causation does not necessarily imply correlation. Implications for improving forecasting are considered with respect to the above issues.     

Estimation and Prediction Tests of Cash Flow Forecast Accuracy

Recently, analysts' cash flow forecasts have become widely available through financial information services. Cash flow information enables practitioners to better understand the real operating performance and financial stability of a company, particularly when earnings information is noisy and of low quality. However, research suggests that analysts' cash flow forecasts are less accurate and more dispersed than earnings forecasts. We thus investigate factors influencing cash flow forecast accuracy and build a practical model to distinguish more accurate from less accurate cash flow forecasters, using past cash flow forecast accuracy and analyst characteristics. We find significant power in our cash flow forecast accuracy prediction models. We also find that analysts develop cash flow‐specific forecasting expertise and knowhow, which are distinct from those that analysts acquire from forecasting earnings. In particular, cash flow‐specific information is more useful in identifying accurate cash flow forecasters than earnings‐specific information.Copyright © 2011 John Wiley & Sons, Ltd.     

Prediction intervals for growth curve forecasts

Since growth curves are often used to produce medium- to long-term forecasts for planning purposes, it is obviously of value to be able to associate an interval with the forecast trend. The problems in producing prediction intervals are well described by Chatfield. The additional problems in this context are the intrinsic non-linearity of the estimation procedure and the requirement for a prediction region rather than a single interval. The approaches considered are a Taylor expansion of the variance of the forecast values, an examination of the joint density of the parameter estimates, and bootstrapping. The performance of the resultant intervals is examined using simulated data sets. Prediction intervals for real data are produced to demonstrate their practical value.     

IGBT 模块故障机理及其预报方法综述

以IGBT模块的内部结构和材料特性为基础,研究了焊料层疲劳、铝键接线断裂或剥离、DCB基片失效等封装级故障产生机理;分析栅氧化层击穿、电过应力、辐射失效等器件级故障的产生机理;最后,分别给出了两大类故障的预报方法。     

The Volatility and Density Prediction Performance of Alternative GARCH Models

This study compares the volatility and density prediction performance of alternative GARCH models with different conditional distribution specifications. The conditional residuals are specified as normal, skewedHyphen;t or compound Poisson (jump) distribution based upon a nonlinear and asymmetric GARCH (NGARCH) model framework. The empirical results for the S&P 500 and FTSE 100 index returns suggest that the jump model outperforms all other models in terms of both volatility forecasting and density prediction. Nevertheless, the superiority of the nonHyphen;normal models is not always significant and diminished during the sample period on those occasions when volatility experiences an obvious structural change. Copyright © 2011 John Wiley & Sons, Ltd.