Study on the Yield Prediction Model of Processing Tomato Based on the Grey System Theory

[Objective] The research aimed to study the yield prediction model of processing tomato based on the grey system theory.[Method] The variation trend of processing tomato yield was studied by using the grey system theory,and GM（1,1）grey model of processing tomato yield prediction was established.The processing tomato yield in Xinjiang during 2001-2009 was as the example to carry out the instance analysis.[Result] The model had the high forecast accuracy and strong generalization ability,and was reliable for the prediction of recent processing tomato yield.[Conclusion] The research provided the reference for the macro-control of tomato industry,the processing and storage of tomato in Xinjiang.

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.

Prediction model of interval grey number based on DGM(1,1)

In grey system theory,the studies in the field of grey prediction model are focused on real number sequences,rather than grey number ones.Hereby,a prediction model based on interval grey number sequences is proposed.By mining the geometric features of interval grey number sequences on a two-dimensional surface,all the interval grey numbers are converted into real numbers by means of certain algorithm,and then the prediction model is established based on those real number sequences.The entire process avoids the algebraic operations of grey number,and the prediction problem of interval grey number is usefully solved.Ultimately,through an example＇s program simulation,the validity and practicability of this novel model are verified.

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.

Prediction of leaching rate in heap leaching process by grey dynamic model GDM(1,1)

The method of developing GM（1,1） model is extended on the basis of grey system theory. Conditions for the transfer function that improve smoothness of original data sequence and decrease the revert error are given. The grey dynamic model is first combined with the transfer function to predict the leaching rate in heap leaching process. The results show that high prediction accuracy can be expected by using the proposed method. This provides a new approach to realize prediction and control of the future behavior of leaching kinetics.

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.

A Novel Rolling and Fractional-ordered Grey System Model and Its Application for Predicting Industrial Electricity Consumption

Accurate and reasonable prediction of industrial electricity consumption is of great significance for promoting regional green transformation and optimizing the energy structure.However,the regional power system is complicated and uncertain,affected by multiple factors including climate,population and economy.This paper incorporates structure expansion,parameter optimization and rolling mechanism into a system forecasting framework,and designs a novel rolling and fractional-ordered grey system model to forecast the industrial electricity consumption,improving the accuracy of the traditional grey models.The optimal fractional order is obtained by using the particle swarm optimization algorithm,which enhances the model adaptability.Then,the proposed model is employed to forecast and analyze the changing trend of industrial electricity consumption in Fujian province.Experimental results show that industrial electricity consumption in Fujian will maintain an upward growth and it is expected to 186.312 billion kWh in 2026.Compared with other seven benchmark prediction models,the proposed grey system model performs best in terms of both simulation and prediction performance metrics,providing scientific reference for regional energy planning and electricity market operation.

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.

Network Security Situation Prediction Based on Improved Adaptive Grey Verhulst Model

Network security situation is a hot research topic in the field of network security. Whole situation awareness includes the current situation evaluation and the future situation prediction. However, the now-existing research focuses on the current situation evaluation, and seldom discusses the future prediction. Based on the historical research, an improved grey Verhulst model is put forward to predict the future situation. Aiming at the shortages in the prediction based on traditional Verhulst model, the adaptive grey parameters and equal- dimensions grey filling methods are proposed to improve the precision. The simulation results prove that the scheme is efficient and applicable.

采用模糊逻辑理论的覆冰厚度预测模型

针对输电线路覆冰对电网造成巨大损害,以及目前覆冰预测研究较少等问题,提出了基于模糊逻辑理论的覆冰厚度预测模型的建立方法。分析了贵州电网8条输电线路的覆冰现场监测数据,初步确定了25条有效模糊规则,并建立了基于模糊逻辑理论的覆冰厚度预测模型。预测模型的验证结果表明:模型预测的覆冰厚度值与现场实际覆冰厚度值的误差<5 mm,该预测模型是可行和有效的。

灰色预测理论在沙漠化监测中的应用

本文在农牧交错带沙漠化信息系统的基础上，根据数据库中的部分内容，以榆林县为例，用灰色系统的理论及ＧＭ（１，１）预测模型，分别对该县的人口及沙漠化土地进行预测研究，研究结果为；到２０００年，榆林县人口为４４．０３万人，比１９７５年净增人口１８．０５万人，占１９７５年总人口的６９．４９％，人口密度达６２．３９人／ｋｍ￣２，比１９７７年联合国沙漠化会议提出的２０人／ｋｍ￣２，为干旱地带的人口对土地压力的临界值高４２．３９人／ｋｍ￣２；榆林县重度、中度、轻度沙漠化土地面积分别为２０．６９６５万ｈａ，１５．０１２６ｈａ和１０．７１０５万ｈａ。严重沙漠化土地面积比１９７５年减少约１９万ｈａ，中度沙漠化土面积比１９７５年增加约４．８万ｈａ，轻度沙漠化土地面积比１９７５年增加５．６万ｈａ，总的趋势是沙漠化土地面积减少。

灰色模型改进的大坝变形分形几何监控模型

为了对大坝安全进行准确监控,利用分形几何理论预测大坝变形。针对一般常维分形分布不能很好分析大坝变形数据的问题,对监测数据进行N阶累计和变换,对变换后的数据利用分段变维分形模型计算各阶变形维数序列,再选择效果较好分形维数已知序列预测未知分形维数,最后反推大坝变形预测数据。针对传统变维分形预测模型分形维数预测方法的不确定性和所需监测数据量大的缺点,利用灰色模型预测分形维数,建立改进的大坝分形几何监控模型。结合工程实例,对比插值法预测分形参数的传统分形几何预测模型和灰色模型改进后的预测模型之间的预测精度,结果表明,改进分形模型不仅在预测精度上有所提高,而且更具稳定性和抗波动性。

长隧道围岩稳定性的模型预测

长隧道围岩级别交替变化,围岩稳定性较差。监控量测是隧道安全施工的重要保证,通过对隧道现场实测数据进行建模评价和稳定性预测,来判断隧道围岩的稳定性,从而指导隧道的下一步施工。本文首先对测量数据进行粗差探测,然后利用灰色系统理论中的GM(1,1)预测模型和BP神经网络原理对测量数据进行建模分析,来预测围岩变形量。

改进灰色模型在刀具状态监控中的研究与应用

刀具状态的精确监控是保证金属切削加工过程顺利进行的关键,因此研制准确、可靠且成本低廉的刀具磨损状态监控系统一直是研究人员所追求的目标。引人改进灰色预测模型理论用来预测刀具的运行状态,具有所需数据少、精度高的优势。预测曲线符合实际,较好地反映了刀具磨损状态的变化,达到了监控的目的。

改进的新灰色模型在边坡变形预测中的运用

通过对边坡表面变形观测系统反馈信息特性的充分认识,采用灰色理论,沿用新陈代谢思想,从优化原理出发,进行逐层推导,得到了具有一定针对性的MAMUGM(1,1,w,η)预测模型,并在工程实例中进行验证,结果表明该模型具有一定模拟和预测精度,对变形非渐变点具有优越的处理能力,通过设置误差限,利用残差控制和样本重构两方面的优化选择,可以使模型能够进一步得到改进,为评估浅表层坡体的稳定提供了依据,具有一定的适用性.

基于突变论的刀具寿命建模与预报的研究

介绍了基于突变论的方法建立刀具磨损与破损失效寿命模型的可行性的研究结果。借助于最小二乘回归，建立了变参数三次多项式车／立铣刀具磨／破损融合模型。利用这一模型完成了一种车／立铣监视系统。实验结果表明，监视刀具磨损和破损的成功率≥９５％，刀具磨损值的预报误差≤１０％。

隧道开挖地表沉降动态预测及影响因素分析

城市地铁隧道施工不可避免地对围岩产生扰动引起地表沉降,动态预测隧道开挖引起的地表沉降是确保地表建构筑物与隧道施工安全的重要基础。针对隧道施工过程中地表沉降难以准确动态预测的问题,在定义纵向开挖度系数γ的基础上,建立横向地表沉降动态预测模型。该模型能够描述同一监测位置沉降曲线随掌子面推进而不断变化的规律,实现施工现场沉降动态预测。结果表明:在特定约束条件下,该模型能够退化为Peck模型以及随机介质理论预测模型;通过现场施工验证了该动态预测模型的准确性与适用性;基于纵向开挖度系数γ将隧道沿纵向分为强烈、中度和轻度共3个影响段,较好地反映了开挖掌子面在不同位置对同一监测截面的影响程度;通过分析建筑物和隔离桩对地表沉降曲线的影响可知,建筑物与其附近地层呈现出协同变形、共同承载的特征,在隧道一侧添加地质钻隔离桩对该侧地表沉降值的减小程度可达71.9%。研究成果对滇中引水工程现场施工与类似工程具有一定指导和借鉴意义。

基于灰色系统理论的高层建筑变形分析应用研究

简述了灰色系统理论及该理论在高层建筑沉降变形监测分析中的应用思路,详细叙述了灰色模型的建模过程[1]。实例中,通过某高层建筑物4个监测点的6期观测数据,完成了模型参数的推导,建立了空间多点灰色预测模型。利用所建立的灰色模型对建筑物形变进行预测模拟,经模拟沉降值和实测数据的对比得出,该模型严密、可靠性好、精度高,该方法可用于建筑物变形的预测模拟。

非等间隔GM(1,1)模型在建筑物沉降中的应用研究

变形监测工作对保障工程建筑物的安全有着重要作用,科学、准确、及时地分析和预报工程建筑物的变形状况,对工程建筑物的施工和后期运营尤为重要。灰色预测理论是一种运用较为广泛的预测分析方法,运用分段低次牛顿插值的方法对非等时间序列进行处理,并以某高层建筑物为例,分析非等间隔GM(1,1)模型在沉降预测中的精度与可行性,最终得出了有益结论。

工作面刮板输送机运行形态监测分析

为了解决回坡底煤矿11-109工作面刮板输送机未知运行形态造成工作面的停产问题,基于机器学习理论,通过滚动预测模型和核方法,对刮板输送机运行形态进行了监测和预测研究,对比分析真实测量值和理论测量值之间的差异,证明了该理论对刮板输送机运行形态监测的可行性,且具有高准确率、高速度的计算特点。