Hybrid grey model to forecast monitoring series with seasonality

The grey forecasting model has been successfully applied to many fields. However, the precision of GM（1,1） model is not high. In order to remove the seasonal fluctuations in monitoring series before building GM （1,1） model, the forecasting series of GM（1,1） was built, and an inverse process was used to resume the seasonal fluctuations. Two deseasonalization methods were presented , i. e. , seasonal index-based deseasonalization and standard normal distribution-based deseasonalization. They were combined with the GM（1,1） model to form hybrid grey models. A simple but practical method to further improve the forecasting results was also suggested. For comparison, a conventional periodic function model was investigated. The concept and algorithms were tested with four years monthly monitoring data. The results show that on the whole the seasonal index-GM（1,1） model outperform the conventional periodic function model and the conventional periodic function model outperform the SND-GM（1,1） model. The mean absolute error and mean square error of seasonal index-GM（1,1） are 30.69 % and 54.53 % smaller than that of conventional periodic function model, respectively. The high accuracy, straightforward and easy implementation natures of the proposed hybrid seasonal index-grey model make it a powerful analysis technique for seasonal monitoring series.     

Grey Smoothing Model for Predicting Mine Gas Emission

A grey smoothing model for predicting mine gas emission was presented by combining the grey system theory with the smoothing prediction technique. First of all, according to the variable sequence, GM (1,1) model was set up to predict the general development trend of variable as first fitted values, then the smoothing prediction technique was used to revise the fitted values so as to improve the accuracy of prediction. The results of application in the No. 6 Coal Mine in Pingdingshan mining area show that the grey smoothing model has higher accuracy than that of GM (1,1) in predicting the variable sequence with strong fluctuation. The research provides a new scientific method for predicting mine gas emission.     

Evaluation of measurement uncertainty based on grey system theory for small samples from an unknown distribution

To evaluate measurement uncertainty for small sample size and measurement data from an unknown distribution, we propose a grey evaluation method of measurement uncertainty based on the grey relation coefficient. The uncertainty of measurement is analyzed using grey system theory, and the defects of the grey evaluation model of measurement uncertainty (GEMU) are studied. We then establish an improved grey evaluation model of measurement uncertainty (IGEMU). Simulations show that the precision of IGEMU is greater than that of GEMU, and that sample size has only a small effect on the precision of IGEVU. In particular, IGEMU is applied to evaluating measurement uncertainty for small sample size and measurement data from an unknown distribution. The measurement uncertainty of total profile deviation, which is measured by the CNC gear measuring center, can be evaluated by a combination of IGEMU and the Monte Carlo method.     

A method for determining the small-sample test length of carrier rocket

To satisfy the demand of test evaluation of carrier rocket, the character of aerocraft and the primary flight error should be well considered. By making full use of tracking data and ground test data, a new method is provided to expand sample size through converting the system error of guidance instruments (SEGI) of special case into standard case. In terms of the prior distribution of the SEGI coefficients, a novel model, named Bayes spline model, is developed for precision evaluation, and its performance is analyzed subsequently. For a given threshold of precision.the test length can be determined by the posterior variance with the afore-mentioned model. Finally, it is proved by theoretical analysis and simulation that this method has clear engineering background and is able to fully exploit the test resources and enables us to draw a more reasonable conclusion.     

A Research on the E-commerce Applied to the Construction of Marketing Model

The function of E-commerce is becoming more and more widely applied to many fields,which bring about some new challenges and opportunities for the construction of marketing model.It is proved that the more E-com- merce applied to the construction of marketing,the more precision of forecast for the enterprises can acquire,which is very helpful for the production and marketing of enterprises.Therefore,the research on the E-commerce applied to the construction of marketing is popular today.This paper applied the five important elements which are product, price,place,advertising and sales promotion,as well as,consumer behavior to construct a marketing model with E-commerce.By the analysis,this paper will draw the conclusions that two problems should be faced.One is that available theories and approaches may be insufficient in tackling many E-commerce problems.And the other is that the construction of marketing model for E-commerce can enrich the fields quite remarkably as the new theories,data and methods which can be applied.     

A new grey forecasting model based on BP neural network and Markov chain

A new grey forecasting model based on BP neural network and Markov chain was proposed. In order to combine the grey forecasting model with neural network, an important theorem that the grey differential equation is equivalent to the time response model, was proved by analyzing the features of grey forecasting model(GM(1,1)). Based on this, the differential equation parameters were included in the network when the BP neural network was constructed, and the neural network was trained by extracting samples from grey system’s known data. When BP network was converged, the whitened grey differential equation parameters were extracted and then the grey neural network forecasting model (GNNM(1,1)) was built. In order to reduce stochastic phenomenon in GNNM(1,1), the state transition probability between two states was defined and the Markov transition matrix was established by building the residual sequences between grey forecasting and actual value. Thus, the new grey forecasting model(MNNGM(1,1)) was proposed by combining Markov chain with GNNM(1,1). Based on the above discussion, three different approaches were put forward for forecasting China electricity demands. By comparing GM(1, 1) and GNNM(1,1) with the proposed model, the results indicate that the absolute mean error of MNNGM(1,1) is about 0.4 times of GNNM(1,1) and 0.2 times of GM(1,1), and the mean square error of MNNGM(1,1) is about 0.25 times of GNNM(1,1) and 0.1 times of GM(1,1).     

Similar material simulation of time series system for induced caving of roof in continuous mining under backfill

With the help of similar material simulation test,time series system for induced caving of roof in continuous mining under complex backfill in ore body No.92 of Tongkeng Tin Mine was studied. According to the similarity theory,a two-dimensional similar simulation test-bed was constructed. The stress and displacement that change along with the advance of mining were acquired and analyzed automatically by data system. The processes of continuous mining of ore-block in 5 intervals and artificial induced caving of roof were simulated. The results of the test show that ore body remained as safety roof in thickness of 15 m guarantees the safe advance of stoping work face. Caving of safety roof puts in practice at the first two mining intervals when the third interval of continuous mining is finished,and one interval as the safety distance should be kept all the time between stopping and caving. While mining in the last interval,pre-slotting should be implemented first of all,and the roof of the last two mining intervals is caved simultaneously. Only this kind of time series system can be an efficient and safe way for induced caving of roof in continuous mining.     

Optimization of satellite searching strategy of the non-stationary antenna

The tiny searching step length and the satellite distribution density are the major factors to influence the efficiency of the satellite finder, so a scientific and reasonable method to calculate the tiny searching step length is proposed to optimize the satellite searching strategy. The pattern clustering and BP neural network are applied to optimize the tiny searching step length. The calculated tiny searching step length is approximately equal to the theoretic value for each satellite. In application, the satellite searching results will be dynamically added to the training samples to re-train the network to improve the generalizability and the precision. Experiments validate that the optimization of the tiny searching step length can avoid the error of locating target satellite and improve the searching efficiency.     

An HHT-based method to eliminate short-time strong disturbance from measured signals of bridge

According to the characteristic that Hilbert-Huang transform (HHT) can detect abnormity in signals, an HHT-based method to eliminate short-time strong disturbance was proposed. The signal with short-time strong disturbance was decomposed into a series of intrinsic mode functions (IMFs) and a residue by the empirical mode decomposition (EMD). The instantaneous amplitudes and frequencies of each IMF were calculated. And at abnormal section, instantaneous amplitudes and frequencies were fitted according to the data at normal section, replacing the fitted data for the original ones. A new set of IMFs was reconstructed by using the processed instantaneous amplitudes and frequencies. For the residue, abnormal fluctuations could be directly eliminated. And a new signal with the short-time strong disturbance eliminated was reconstructed by superposing all the new IMFs and the residue. The numerical simulation shows that there is a good correlation between the reconstructed signal and the undisturbed signal. The correlation coefficient is equal to 0.999 1. The processing results of the measured strain signal of a bridge with short-time strong disturbance verify the practicability of the method.     

Vibration velocity and frequency of underwater short-hole blasting

Based on the measuring data of underwater blasting vibration and the regression analysis results of these data, two formulae usually used of blasting vibration velocity were compared. Factors that can affect blasting vibration and frequency were summarized and analyzed. It is thought that the effect of the number of freedom face and burden direction on blasting vibration should be considered during blasting design. Based on the relevant research results and the regression results of these data, a formula to calculate under water blasting frequency was put forward.     

非等间距灰色模型在沉降预测中的应用

在灰色模型GM(1.1)的基础上,建立了非等间距灰色模型,用于高层建筑沉降预测。非等间距模型是以等间距数列为基础,把非等间距数列转化为等间距数列,再进行一次累加生成处理,进而建立GM(1.1)模型,并介绍了其精度评定的方法。结合高层建筑沉降监测工程实例进行计算分析,预测精度较高。实践证明,非等间距灰色预测模型在沉降预测中具有较高的应用价值。     

非等间距新息GM（1，1）模型及其应用

针对非等间距GM（1,1）模型建模精度低、适应性不强等问题,应用新信息优化原理及灰色系统建模方法,采用原始数据序列的第n个分量作为灰色微分方程的初始条件,提出了非等间距新息GM（1,1）模型.基于背景值是影响灰色建模精度的重要因素之一,对非等间距新息GM（1,1）模型的背景值构造进行了研究,根据灰色模型的指数特性和积分特点,利用非齐次指数函数来拟合一次累加生成序列,重构非等间距新息GM（1,1）模型的背景值,并给出了背景值构造公式.该背景值不仅适用于等间距新息建模型,也适合于非等间距新息建模型,具有精度高、适应性强等特点.实例表明,所建模型具有良好的实用性和可靠性.     

倒数累加生成灰色GRM（1，1）模型的改进

GRM(1,1)是适用于非负递减数列的一种灰色预测模型.它通过对原始数据列的倒数累加生成变换及对离散点处灰导数背景值的加权处理,改善了GM(1,1)的模型精度.尝试通过对离散点处灰导数的加权处理,来改进倒数累加灰色模型GRM(1,1)的精度.实例表明,改进的倒数累加灰色模型在模型精度和预测精度上都较原模型有了很大的提高.     

动态新息GM(1,1)在卫星电池阵功率预测中的应用

根据观测到的历史数据对卫星理发池阵输出功率的未来值进行预测，可实现对电池阵性能劣化的早期预报。灰色预测方法的GM(1，1)模型只适合对单调递增或递减时间序列进行预测，针对卫星电池阵输出功率具有波动变化的趋势特征，采用实时在线的方法，建立了动态新息GM(1，1)预测模型，经实例预测验证，动态新息GM（1，1）模型可明显地提高预测精度，并且能对电池阵输出功率的波动变化趋势正确预测。该建模方法对其他非单调时间序列的工程预测也具有参考价值。     

基于GM(1,1)模型预测海底管道腐蚀速率软件的开发

针对海底油气管道的腐蚀速率,提出了基于灰色理论的海底管道的腐蚀速率预测方法.在测量数据较少的情况下,利用GM(1,1)可以预测海底管道的腐蚀速率.基于该方法,采用VS2010编制了管线腐蚀速率四数据、五数据及新陈代谢模型的预测软件,为在原始数据较少的条件下确定油气管道腐蚀检测周期提供了不同的检测途径,并通过实验数据进行...     

新陈代谢GM(1,1)模型在建筑物沉降预测中的应用

利用MATLAB7.0软件对原始数据进行等间距处理后,用一次累加数列与原始数列构建微分模型,通过不断去掉旧数据加入新数据,以工程数学为基础,运用灰色理论构建新陈代谢GM(1,1)模型。并以工程实例进行模拟和预测效果检验,将普通GM(1,1)模型和新信息GM(1,1)模型预测效果进行比较,计算和对比结果表明,新陈代谢GM(1,1)模型精度明显高于其它模型,预测效果大大提高。     

基于函数变换的优化灰色预测模型及其应用

基于函数变换理论,提出了对建模数据进行ax+b(a<0,ax+b>0)变换,理论上证明了这种变换可以有效提高建模数据序列的光滑度。并把此方法应用于我国市均医院床位数的建模中,应用结果表明,该方法所建GM(1,1)模型的精度优于传统GM(1,1)模型的精度,从而拓宽了灰色模型的应用范围。     

基于灰色系统的机床热误差建模研究

针对机床加工过程中的热变形误差受多因素影响,变化趋势复杂,难以用常规预测方法进行有效预测的问题,该文提出了一种新的基于改进灰色系统的智能预测模型。该模型利用函数变换法改善灰色系统数据序列的光滑度,采用等维新陈代谢法克服了传统的灰色预测模型的不足,所建模型具备了输入数据动态更新的能力,预测更趋于合理。将该模型应用于工厂现场的一台数控车削加工中心进行热误差趋势的预测,从而实现热误差的补偿研究。研究表明,该模型的预测性能优于全数据GM(1,1)模型和新信息GM(1,1)模型,是运用灰色系统理论进行机床热误差补偿建模最理想的模型,具有优异的补偿功能,能够有效的提高机床加工精度。