Grey GM(1,1) Model with Function-Transfer Method for Wear Trend Prediction and its Application

Trend forecasting is an important aspect in fault diagnosis and work state supervision. The principle, where Grey theory is applied in fault forecasting, is that the forecast system is considered as a Grey system; the existing known information is used to infer the unknown information's character, state and development trend in a fault pattern, and to make possible forecasting and decisions for future development. It involves the whitenization of a Grey process. But the traditional equal time interval Grey GM (1,1) model requires equal interval data and needs to bring about accumulating addition generation and reversion calculations. Its calculation is very complex. However, the non equal interval Grey GM (1,1) model decreases the condition of the primitive data when establishing a model, but its requirement is still higher and the data were pre processed. The abrasion primitive data of plant could not always satisfy these modeling requirements. Therefore, it establishes a division method suited for general data modeling and estimating parameters of GM (1,1), the standard error coefficient that was applied to judge accuracy height of the model was put forward; further, the function transform to forecast plant abrasion trend and assess GM (1,1) parameter was established. These two models need not pre process the primitive data. It is not only suited for equal interval data modeling, but also for non equal interval data modeling. Its calculation is simple and convenient to use. The oil spectrum analysis acted as an example. The two GM (1,1) models put forward in this paper and the new information model and its comprehensive usage were investigated. The example shows that the two models are simple and practical, and worth expanding and applying in plant fault diagnosis.

Application of Grey System GM (1,1) model and unary linear regression model in coal consumption of Jilin Province

The data on the coal production and consumption in Jilin Province for the last ten years were collected,and the Grey System GM( 1,1) model and unary linear regression model were applied to predict the coal consumption of Jilin Production in 2014 and 2015. Through calculation,the predictive value on the coal consumption of Jilin Province was attained,namely consumption of 2014 is 114. 84 × 106 t and of 2015 is 117. 98 ×106t,respectively. Analysis of error data indicated that the predicted accuracy of Grey System GM( 1,1) model on the coal consumption in Jilin Province improved 0. 21% in comparison to unary linear regression model.

基于多元回归和GM的新能源汽车保有量预测

近几年国家大力推动新能源汽车发展。本文基于多元回归模型和GM模型建立新能源汽车保有量预测模型。首先,对新能源汽车保有量影响因素进行筛选,最终选取5个(公共充电桩保有量、老龄化率、国内生产总值、城镇化率、汽车驾驶员人数)作为本文影响因素,然后选取2013~2022年新能源汽车保有量和影响因素数据建立多元回归模型,再利用GM模型对2023~2025年的数值进行预测,将影响因素预测值代入多元回归模型,最终得出2023~2025年新能源汽车保有量分别为1909.61、2964.52、4576.94万辆。为检验模型准确性,本文首先选取2013~2019年的数据建立新的多元回归模型,并利用GM模型预测2020~2022年影响因素数据,将其代入新多元回归模型算出2020~2022年新能源汽车保有量,再与真实值相比较,最终得出平均误差为8.6%。同时利用GM模型对2020~2022年新能源汽车保有量进行预测,得出平均误差为27.5%,远高于本文建立模型所得出的平均误差。

Improvement and application of GM(1,1) model based on multivariable dynamic optimization

For the classical GM(1,1)model,the prediction accuracy is not high,and the optimization of the initial and background values is one-sided.In this paper,the Lagrange mean value theorem is used to construct the background value as a variable related to k.At the same time,the initial value is set as a variable,and the corresponding optimal parameter and the time response formula are determined according to the minimum value of mean relative error(MRE).Combined with the domestic natural gas annual consumption data,the classical model and the improved GM(1,1)model are applied to the calculation and error comparison respectively.It proves that the improved model is better than any other models.

The softening prediction of HSLA steel heat-affected zone based on the grey system

The high-strength low-alloy（ HSLA ） steel heat-affected zone （HAZ）softening was predicted using a grey model. HSLA steel DILLIMAX690E, NK-HITEN61OU2 and BHW35 were taken as examples in the research on ultra-narrow gap automatic welding technology. Test results turned out to be that the errors between the values calculated by the Grey Model （GM） （ 1,1 ） model and their actual value were less than 2%, indicating that the grey prediction method could accurately reflect the actual situation of the high-strength low-alloy steel heat-affected zone softening. This method will play a crucial role in guiding the applications of HSLA steel welded structures in the future.

Grey System Judgment on Reliability of Mechanical Equipment

he Grey system theory -was applied in reliability analysis of mechanical equip-ment. It is a new theory and method in reliability engineering of mechanical engineering of mechanical equipment. Through the Grey forecast of reliability parameters and the reliability forecast of parts and systems, decisions were made in the real operative state of e-quipment in real time. It replaced the old method that required mathematics and physical statistics in a large base of test data to obtain a pre-check , and it was used in a practical problem. Because of applying the data of practical operation state in real time, it could much more approach the real condition of equipment; it-was applied to guide the procedure and had rather considerable economic and social benefits.

A self-adaptive grey forecasting model and its application

GM(1,1)models have been widely used in various fields due to their high performance in time series prediction.However,some hypotheses of the existing GM(1,1)model family may reduce their prediction performance in some cases.To solve this problem,this paper proposes a self-adaptive GM(1,1)model,termed as SAGM(1,1)model,which aims to solve the defects of the existing GM(1,1)model family by deleting their modeling hypothesis.Moreover,a novel multi-parameter simultaneous optimization scheme based on firefly algorithm is proposed,the proposed multi-parameter optimization scheme adopts machine learning ideas,takes all adjustable parameters of SAGM(1,1)model as input variables,and trains it with firefly algorithm.And Sobol’sensitivity indices are applied to study global sensitivity of SAGM(1,1)model parameters,which provides an important reference for model parameter calibration.Finally,forecasting capability of SAGM(1,1)model is illustrated by Anhui electricity consumption dataset.Results show that prediction accuracy of SAGM(1,1)model is significantly better than other models,and it is shown that the proposed approach enhances the prediction performance of GM(1,1)model significantly.

Grey forewarning and prediction for mine water inflowing catastrophe periods

Based on the theory of grey system, established GM （1, 1） grey catastrophe predict model for the first time in order to forecast the catastrophe periods of mine water inflowing （not the volume of water inflowing）. After establishing the grey predict system of the catastrophe regularity of 10 month-average volume of water inflowing, the grey forewarning for mine water inflowing catastrophe periods was established which was used to analyze water disaster in 400 meter level of Wennan Colliery. Based on residual analysis, it shows that the result of grey predict system is almost close to the actual value. And the scene actual result also shows the reliability of prediction. Both the theoretical analysis and the scene actual result indicate feasibility and reliability of the method of grey catastrophe predict system.

Prediction of the maximum water inflow in Pingdingshan No.8 mine based on grey system theory

In order to prevent and control the water inflow of mines, this paper built a new initial GM（1, 1） model to torecast the maximum water inflow according to the principle of new information. The effect of the new initial GM（1, 1） model is not ideal by the concrete example. Then according to the principle of making the sum of the squares of the difference between the calculated sequences and the original sequences, an optimized GM（1, I） model was established. The result shows that this method is a new prediction method which can predict the maximum water inflow accurately. It not only conforms to the guide- line of prevention primarily, but also provides reference standards to managers on making prevention measures.

基于灰色GM(1,1)的农业机械化水平预测模型

根据1986～2005年我国农业机械化综合水平统计数据，并对由于我国耕地面积统计滞后使得在机耕水平计算中存在误差进行了合理分析和修正，结合数据平滑处理，建立了基于灰色GM（1，1）的我国农业机械化综合水平预测模型。通过残差检验和后验差检验方法对预测结果进行了检验，模型拟合精度较好。采用模型对2006年我国农业机械化综合水平值进行预测，结果表现出较高的预测精度，进一步验证了所建模型的可行性。运用该模型对我国2007～2020年间农业机械化综合水平进行预测，结果表明到2020年我国综合机械化水平将达到68％左右。通过定性分析及与其他预测结果比较，模型表现出较好的预测能力。

灰色系统GM(1,1)模型在天津地下热水水位预测中的应用

天津是我国开发利用地热资源较早的城市之一,二十世纪九十年代开始大规模的将地热资源应用于供暖、洗浴、养殖等方面。但随着地热资源开采规模的不断扩大,地下热水的水位正在逐年下降。通过整理和分析以往的动态监测资料,介绍了天津地区地热地质条件,总结了天津地区地下热水水位动态变化的基本特征,并利用灰色系统GM(1,1)模型对地下热水的未来水位变化趋势进行了预测,提出GM(1,1)模型在地下热水水位短期预测中的优势和应用前景。

基于灰色GM(1,1)模型的河南省城镇化水平预测

首先根据河南省人口城镇化数据的特点,将2008—2012年的城镇化水平作为原始数据,建立河南省人口城镇化水平灰色预测模型;其次采用残差估计进行模型检验,结果表明该模型的预测结果较好;最后,根据GM(1,1)模型预计2020年河南省城镇化水平将达到58%左右.

Predicting changes in Bitcoin price using grey system theory

Bitcoin is currently the leading global provider of cryptocurrency.Cryptocurrency allows users to safely and anonymously use the Internet to perform digital currency transfers and storage.In recent years,the Bitcoin network has attracted investors,businesses,and corporations while facilitating services and product deals.Moreover,Bitcoin has made itself the dominant source of decentralized cryptocurrency.While considerable research has been done concerning Bitcoin network analysis,limited research has been conducted on predicting the Bitcoin price.The purpose of this study is to predict the price of Bitcoin and changes therein using the grey system theory.The first order grey model(GM(1,1))is used for this purpose.It uses a firstorder differential equation to model the trend of time series.The results show that the GM(1,1)model predicts Bitcoin’s price accurately and that one can earn a maximum profit confidence level of approximately 98%by choosing the appropriate time frame and by managing investment assets.

A strip thickness prediction method of hot rolling based on D_S information reconstruction

To improve prediction accuracy of strip thickness in hot rolling, a kind of Dempster/Shafer(D_S) information reconstitution prediction method(DSIRPM) was presented. DSIRPM basically consisted of three steps to implement the prediction of strip thickness. Firstly, iba Analyzer was employed to analyze the periodicity of hot rolling and find three sensitive parameters to strip thickness, which were used to undertake polynomial curve fitting prediction based on least square respectively, and preliminary prediction results were obtained. Then, D_S evidence theory was used to reconstruct the prediction results under different parameters, in which basic probability assignment(BPA) was the key and the proposed contribution rate calculated using grey relational degree was regarded as BPA, which realizes BPA selection objectively. Finally, from this distribution, future strip thickness trend was inferred. Experimental results clearly show the improved prediction accuracy and stability compared with other prediction models, such as GM(1,1) and the weighted average prediction model.

灰色GM(1,1)模型在地下水位预测中的应用

GM(1,1)模型是一种精度较高的预测模型,该模型不仅操作简单,而且当数据较少、且无法采用统计和其他的预测方法时,该方法是非常有效的。以某地2000—2005年地下水位为原始数据,采用GM(1,1)模型对2006年地下水位进行了预测,达到为该地区的地下水资源的调配提供参考依据。

应用优化GM(1,1)模型的农民消费水平预测研究

为了使传统灰色GM(1,1)模型的背景值和灰度值更加协调,对原始数据序列和模型背景值进行优化。基于优化的灰色GM(1,1)模型,选取2000—2014年我国农民消费水平数据,预测未来几年我国农民消费水平。结果表明:优化GM(1,1)模型的背景值能减小预测误差,提高预测准确精度,非常适合进行中短期预测;同时,未来几年我国农民消费水平将继续保持稳步增长,到2020年我国农民消费水平将达到19 303元,这将有利于实现我国经济稳步增长。

GM(1,1)模型在丰县地下水水位预测中的应用研究

灰色GM(1,1)模型是基于灰色系统理论的一种预测方法,具有对历史样本数量要求少、计算简便、验证方便等优点,在诸多领域得到广泛应用。本文建立GM(1,1)模型对江苏省丰县III承压地下水水位埋深进行预测,详细阐述了地下水水位埋深时间序列的GM(1,1)模型的原理和建立过程,根据模型的预测值和实测值,对模型的精度进行检验,并对预测结果进行了分析。结果表明:丰县各水位埋深监测点GM(1,1)数学模型精度均达到I级,预测2018-2020年丰县地下水水位呈现逐年下降趋势,水位降落漏斗将不断扩大。丰县应积极寻求地表水源,在徐州市区域供水一体化形势下尽快启用小沿河地表水,科学合理开发利用地下水资源,保证区域水资源均衡发展。

GM(1,1)模型优化及在地下水位预测中的应用

地下水水位变化特征是研究区域地下水的基础,采用优化GM(1,1)模型对温州市永强平原地下水水位进行模拟和预测,优化模型后验差比值和平均相对误差均明显低于GM(1,1)模型,表明优化模型在预测精度上有很大提高,可靠性更好,为地下水水位预测提供了一条新途径。

黑龙江省农业机械化作业水平预测方法

黑龙江省农机化作业水平的预测是一个复杂的非线性系统,其发展变化具有增长性和波动性,对于拟合的方法要求较高。该文对黑龙江省农机化作业水平预测方法进行研究,在传统预测模型灰色GM(1,1)模型、平滑预测模型和回归预测模型的基础上建立了基础预测模型,并与BP神经网络模型组合,建立了灰色神经网络、平滑回归神经网络等组合预测模型,并预测了黑龙江省2008～2015年的农机化耕、种、收、植保、灌溉作业水平。结果表明,新的预测方法拟合精度高、有效、可行,为农机化作业水平的预测提供了一条新的途径;黑龙江省机耕、机播、植保作业水平很高,但是机收作业水平不高,机械化灌溉是主要的瓶颈,需要进一步发展。

抽油井间抽周期的灰色预测

科学地确定间抽周期是间抽井提高生产效率的重要保证。间抽周期受地层、流体、工作制度等多种因素影响,而且各个因素之间关系复杂,因此有必要把抽油井看成一个系统。根据该系统的特点,文中首先对关井阶段和开井阶段的液面进行监测,然后采用灰色GM(1,1)模型建模,从而确定科学的间抽周期,同时,根据灰色系统理论中的"新信息优先"原理,在生产过程中,用新测试点代替旧测试点,实现了模型的实时调整以及间抽周期的实时预测,最后,以实例证明了该方法方便可行。