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。     

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。     

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。     

基于粗糙集与灰色动态预测模型在电力负荷短期预测的应用

针对电力系统多因素负荷预测问题的复杂性,结合粗糙集理论与GM（1,N）模型各自的优势,提出一种基于粗糙集理论的GM（1,N）预测模型.采取粗糙集理论对影响负荷预测因素进行简约,利用GM（1,N）建立简约后的因素变量和负荷之间的关系建立模型,并与GM（1,1）预测模型进行了比较,结果反映基于粗糙集理论的GM（1,N）预测模型的优越性,精准度达到94.055%.     

基于支持向量机回归与分源预测法的瓦斯涌出量预测模型及其应用

针对目前瓦斯涌出量预测模型存在的局限性及精度低等问题,应用分源预测和支持向量机(SVM)的基本原理,将SVM回归与分源预测法相结合,并利用SVM对回采工作面的瓦斯涌出量进行回归分析和数值模拟,建立了SVM分源预测的数学模型,提出了SVM分源预测的新方法。数值实验表明,将训练成功的SVM模型对现场数据进行回归预测并对比预测结果与实际值发现,SVM比BP神经网络预测精度更高,训练样本期望输出与实际值的最大相对误差为1.45%,小于实际要求的5%,准确率较高,预测风险低,可以满足实际要求。     

基于改进粒子群-GRNN模型的地铁隧道沉降预测

为了实现地铁隧道沉降的准确预测,针对传统方法和常用智能方法预测精度不高、适用性不强等问题,建立基于改进粒子群和广义回归神经网络的隧道沉降预测模型。模型引入随机变异因子以克服粒子群算法早熟收敛和后期搜索效率不高的缺陷。通过与GRNN、普通PSO-GRNN和PSO-BP模型进行对比,验证了改进算法的有效性和所建模型的优越性。以长沙地铁隧道为例进行沉降预测发现:预测值与实测值相差1.04 mm,相对误差为4.05%,预测精度高,满足工程需要。     

顺煤层水力割缝抽采煤层瓦斯渗流规律数值模拟

为得到顺煤层水力割缝抽采瓦斯渗流规律以及预测水力割缝后瓦斯气体抽采量，结合渗流力学和弹塑性力学理论，建立了水力割缝抽采煤层瓦斯的固流耦合数学模型，同时给出了相应初始条件及边界条件。利用Madab计算得到顺煤层水力割缝后煤体应力场、瓦斯压力场以及瓦斯抽采量变化规律。数值模拟结果显示：水力割缝后，煤体有效体积应力得到释放；沿割缝方向储层卸压效果明显；煤层内裂隙、裂缝数量增加，长度和张开度增大；煤体渗透性能增强；煤层气抽采量较普通钻孔有较大幅度的提高。模拟结果显示了顺煤层水力割缝抽采煤层气的优势。此模拟方法对煤层气增产工业中确定水力割缝工艺参数以及预测瓦斯产量具有重要的指导意义。     

松软煤层壁式密封瓦斯抽采技术研究

高瓦斯低透气性松软顺层瓦斯抽采过程中,由于钻孔极易发生缩径、塌孔等工程问题,钻孔周围漏风严重,钻孔服务时间短,瓦斯抽采难度大且周期较长等成为瓦斯抽采的主要技术难题。通过分析松软煤层瓦斯抽采存在的关键问题,应用多孔介质渗流力学、流体平衡等理论及钻孔护孔原理,提出了松软煤层的固液耦合壁式密封技术。现场研究表明,应用固液耦合壁式密封技术抽采松软煤层瓦斯有效地阻止了巷道风流的漏入,极大地提高了瓦斯抽采效率,使得单孔瓦斯抽采浓度达90%以上,与煤矿所使用的聚氨酯袋配合密封液封孔技术相比,单孔纯瓦斯流量提高2~3倍,且抽采过程浓度稳定,对松软煤层瓦斯的高效抽采及实现煤与瓦斯共采和煤层气绿色开发理念具有重要意义。     

基于RBF神经网络的烧结终点预测模型

本文提出一种基于径向基函数(RBF)神经网络的烧结终点预测模型.该模型首先采用改进的最近邻聚类算法确定径向基函数中心,接着应用递推最小二乘法训练网络的权值.通过现场采集数据对该模型进行仿真,其实验结果表明,该模型具有较好的学习能力和泛化能力,为烧结终点的预测提供了一种新的解决方法.     

基于MATLAB的煤与瓦斯突出BP网络预测方法

影响煤与瓦斯突出的各种要素与突出现象之间的关系复杂,且具有明显的非线性特点.BP人工神经网络模型可以很好地逼近这种非线性函数关系.基于煤与瓦斯突出特征指标的分析,建立了合理的单隐层结构的BP预测模型,并利用MATLAB神经网络工具箱实现了模型的训练与预测,应用结果表明,这种突出预测方法具有很高的计算效率和预测精度.     

A deep residual compensation extreme learning machine and applications

The extreme learning machine (ELM) is a type of machine learning algorithm for training a single hidden layer feedforward neural network. Randomly initializing the weight between the input layer and the hidden layer and the threshold of each hidden layer neuron, the weight matrix of the hidden layer can be calculated by the least squares method. The efficient learning ability in ELM makes it widely applicable in classification, regression, and more. However, owing to some unutilized information in the residual, there are relatively huge prediction errors involving ELM. In this paper, a deep residual compensation extreme learning machine model (DRC-ELM) of multilayer structures applied to regression is presented. The first layer is the basic ELM layer, which helps in obtaining an approximation of the objective function by learning the characteristics of the sample. The other layers are the residual compensation layers in which the learned residual is corrected layer by layer to the predicted value obtained in the previous layer by constructing a feature mapping between the input layer and the output of the upper layer. This model is applied to two practical problems: gold price forecasting and airfoil self-noise prediction. We used the DRC-ELM with 50, 100, and 200 residual compensation layers respectively for experiments, which show that DRC-ELM does better in generalization and robustness than classical ELM, improved ELM models such as GA-RELM and OS-ELM, and other traditional machine learning algorithms such as support vector machine (SVM) and back-propagation neural network (BPNN).     

神经树在煤与瓦斯突出预测中应用研究

由于煤与瓦斯突出影响因素之间存在着复杂的非线性关系,为准确预测煤与瓦斯突出的危险性,本文提出了基于柔性神经树的煤与瓦斯突出预潮模型,其中利用多表达式编程和粒子群优化算法分别优化了自身的结构及相关参数,使得神经树具有强大的预测和分类能力,与传统神经网络相比具有更加灵活的自动优化能力.通过采用实测数据对算法进行了验证. 结果 表明与常规预测方法相比较,该模型的预测准确性高,具有良好的适应性和有效性.     

Stock index prediction based on wavelet transform and FCD-MLGRU

With the development of artificial intelligence, deep learning is widely used in the field of nonlinear time series forecasting. It is proved in practice that deep learning models have higher forecasting accuracy compared with traditional linear econometric models and machine learning models. With the purpose of further improving forecasting accuracy of financial time series, we propose the WT-FCD-MLGRU model, which is the combination of wavelet transform, filter cycle decomposition and multilag neural networks. Four major stock indices are chosen to test the forecasting performance among traditional econometric model, machine learning model and deep learning models. According to the result of empirical analysis, deep learning models perform better than traditional econometric model such as autoregressive integrated moving average and improved machine learning model SVR. Besides, our proposed model has the minimum forecasting error in stock index prediction.     

Computing the variance of the forecast error for the holt-winters seasonal models

This paper presents expressions for the variance of the forecast error for arbitrary lead times for both the additive and multiplicative Holt-Winters seasonal forecasting models. It is shown that even when the smoothing constants are chosen to have values between zero and one, when the period is greater than four, the variance may not be finite for some values of the smoothing constants. In addition, the regions where the variance becomes infinite are almost the same for both models. These results are of importance for practitioners, who may choose values for the smoothing constants arbitrarily, or by searching on the unit cube for values which minimize the sum of the squared errors when fitting the model to a data set. It is also shown that the variance of the forecast error for the multiplicative model is nonstationary and periodic.     

Forecasting the Dubai financial market with a combination of momentum effect with a deep belief network

Applying recent advances in machine learning techniques, we propose a hybrid model to forecast the Dubai financial market general index. Particularly, we exploit a deep belief networks model that applies a restricted Boltzmann machine as its main component in combination with momentum effects. We also introduce an innovative way of selecting the inputs by using momentum effects. With this hybrid methodology we generate a prediction model along with a comparison of three different linear models. The results obtained from the hybrid model are better and more stable than the three linear models. The findings support that the hybrid model we applied will find their way into finance because of their reliability and good performance.     

A novel hybrid fine particulate matter (PM2.5) forecasting and its further application system: Case studies in China

Air pollution has received more attention from many countries and scientists due to its high threat to human health. However, air pollution prediction remains a challenging task because of its nonstationarity, randomness, and nonlinearity. In this research, a novel hybrid system is successfully developed for PM_2.5 concentration prediction and its application in health effects and economic loss assessment. First, an efficient data mining method is adopted to capture and extract the primary characteristic of PM_2.5 dataset and alleviate the noises' adverse effects. Second, Harris hawks optimization algorithm is introduced to tune the extreme learning machine model with high prediction accuracy, then the optimized extreme learning machine can be established to obtain the forecasting values of PM_2.5 series. Next, PM_2.5-related health effects and economic costs was estimated based on the predicted PM_2.5 values, the related health effects, and environmental value assessment methods. Several experiments are designed using three daily PM_2.5 datasets from Beijing, Tianjin, and Shijiazhuang. Lastly, the corresponding experimental results showed that this proposed system can not only provide early warning information for environmental management, assist in the formulation of effective measures to reduce air pollutant emissions, and prevent health problems but also help for further research and application in different fields, such as health issues due to PM_2.5 pollutant.     

Volatility forecasting for stock market incorporating macroeconomic variables based on GARCH-MIDAS and deep learning models

Empirical experiments have shown that macroeconomic variables can affect the volatility of stock market. However, the frequencies of macroeconomic variables are low and different from the stock market volatility, and few literature considers the low-frequency macroeconomic variables as input indicators for deep learning models. In this paper, we forecast the stock market volatility incorporating low-frequency macroeconomic variables based on a hybrid model integrating the deep learning method with generalized autoregressive conditional heteroskedasticity and mixed data sampling (GARCH-MIDAS) model to process the mixing frequency data. This paper firstly takes macroeconomic variables as exogenous variables then uses the GARCH-MIDAS model to deal with the problem of different frequencies between the macroeconomic variables and stock market volatility and to forecast the short-term volatility and finally takes the predicted short-term volatility as the input indicator into machine learning and deep learning models to forecast the realized volatility of stock market. It is found that adding macroeconomic variables can significantly improve the forecasting ability in the comparison of the forecasting effects of the same model before and after adding the macroeconomic variables. Additionally, in the comparison of the forecasting effects among different models, it is also found that the forecasting effect of the deep learning model is the best, the machine learning model is worse, and the traditional econometric model is the worst.     

Assessment of agricultural energy consumption of Turkey by MLR and Bayesian optimized SVR and GPR models

Agricultural productivity highly depends on the cost of energy required for cultivation. Thus prior knowledge of energy consumption is an important step for energy planning and policy development in agriculture. The aim of the present study is to evaluate the application potential of multiple linear regression (MLR) and machine learning tools such as support vector regression (SVR) and Gaussian process regression (GPR) to forecast the agricultural energy consumption of Turkey. In the development of the models, widespread indicators such as agricultural value-added, total arable land, gross domestic product share of agriculture, and population data were used as input parameters. Twenty-eight-year historical data from 1990 to 2017 were utilized for the training and testing stages of the models. A Bayesian optimization method was applied to improve the prediction capability of SVR and GPR models. The performance of the models was measured by various statistical tools. The results indicated that the Bayesian optimized GPR (BGPR) model with exponential kernel function showed a superior prediction capability over MLR and Bayesian optimized SVR model. The root mean square error, mean absolute deviation, mean absolute percentage error, and coefficient of determination (R²) values for the BGPR model were determined as 0.0022, 0.0005, 0.2041, and 0.9999 in the training phase and 0.0452, 0.0310, 7.7152, and 0.9677 in the testing phase, respectively. As a result, it can be concluded that the proposed BGPR model is an efficient technique and has the potential to predict agricultural energy consumption with high accuracy.