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.

Forecasting Model of Coal Requirement Quantity Based on Grey System Theory

The generally used methods of forecasting coal requirement quantity include the analogy method, the outside push method and the cause effect analysis method. However, the precision of forecasting results using these methods is lower. This paper uses the grey system theory, and sets up grey forecasting model GM (1, 3) to coal requirement quantity. The forecasting result for the Chinese coal requirement quantity coincides with the actual values, and this shows that the model is reliable. Finally, this model are used to forecast Chinese coal requirement quantity in the future ten years.

Investigation of the Interactive,Intimidating Relation Between Urbanization and the Environment in an Arid Area Based on Grey System Theory

Taking the Hexi Corridor in western China as an example, this paper studies the interactive intimate i relation between urbanization and the environment in add areas based on the grey system theory. The results show that the grey relational degree between urbanization and the environment is low in the agriculture-oriented cities, modest in the tourism-oriented cities and great in the industry-oriented cities. The changing trend of coupling degree between urbanization and the environment does not entirely agree with that of urbanization or the environment. It showed fluctuating trends, which reflects the compactness and properties of the different stages of the coupling states between urbanization and the environment. In order to achieve a harmonious development with the environment in add areas, traditional development ideas about urbanization should be revised and more attention should be paid to the effect of restriction of water resources and the ecological environment on the development of the economy and society.

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.

Optimization approach hydroforming car beam billets based grey system theory

Perfect combination of structural size parameters of the hydroforming billets is essential to obtain even wall thicknesses of the car beam. Finite element （ FE ） analysis on hydroforming car beam was carried out, and the results were optimized according to multiple quality objectives by the grey system theory. With bending angle, bending radius and hight difference along the axis direction as variables, orthogonal FE analyses were conducted and the minimum and maximum wall thicknes ses of the billets with different sizes were obtained. Taking the minimum and maximum wall thick nesses as two references, the correlation coefficient between the data for reference and those for comparison by the grey system theory reduced multi objectives to a single quality objective, and the average correlation level of every billet facilitated the optimization of size parameters for hydroform ing car beam. The trial production showed that the optimization approach satisfied the need of hy droforming car beams.

Probability estimation based on grey system theory for simulation evaluation

In the evaluation of some simulation systems, only small samples data are gotten due to the limited conditions. In allusion to the evaluation problem of small sample data, an interval estimation approach with the improved grey confidence degree is proposed.On the basis of the definition of grey distance, three kinds of definition of the grey weight for every sample element in grey estimated value are put forward, and then the improved grey confidence degree is designed. In accordance with the new concept, the grey interval estimation for small sample data is deduced. Furthermore,the bootstrap method is applied for more accurate grey confidence interval. Through resampling of the bootstrap, numerous small samples with the corresponding confidence intervals can be obtained. Then the final confidence interval is calculated from the union of these grey confidence intervals. In the end, the simulation system evaluation using the proposed method is conducted. The simulation results show that the reasonable confidence interval is acquired, which demonstrates the feasibility and effectiveness of the proposed method.

Selecting Suitable Heat Source in Refinery for Multi-effect Distillation Based on Grey System Theory

For dealing with high-salinity wastewater in the refinery, the high cost of driving heat source is the disadvantage of multi-effect distillation (MED) so it is of great importance to evaluate the performance of low-temperature heat source for conducting MED and select the optimal temperature for it. Both the MED and the low-temperature heat sources studied in this paper were from a typical refinery located in northwestern China. Besides, a new methodology to evaluate heat sources as the optimal candidate was proposed for MED based on the grey system theory. Five process units, which included 18 fluids of the refinery, were named as the evaluation projects. Three factors, which included safety effects, total costs and characteristics of low-temperature heat sources were determined as the evaluation indexes, the values of which were established through the analyses. The results obtained through the grey correlation analyses have revealed that the grey correlation degrees of these units were 0.661(AVDU), 0.732 (#1 FCCU), 0.618 (#2 FCCU), 0.535 (#1 DCU), and 0.572 (#2 DCU), respectively. Thus, the optimal heat source was provided from #1 FCCU. Through further analyses of the fluids from #1 FCCU, the grey correlation degrees of the fluids were 0.597 (oil and gas at top of tower), 0.714 (recycle oil and gas), and 0.512 (diesel), respectively. Thus, the optimal heat source was the oil and gas recycle stream.

Using Grey System Theory for Earthquake Forecast

By combining conventional grey correlation analysis, grey clustering method and grey forecasting methods with our multi-goal forecast thoughts and the techniques of grey time series processing, we develop six different grey earthquake forecast models in this paper. Using the record of major earthquakes in Japan from 1872 to 1995, we forecast future earthquakes in Japan. We develop an earthquake forecast model. By using the major earthquakes in Japan from 1872 to 1984, we forecast earthquakes from 1985 to 1995 and check the precision of the grey earthquake models. We find that the grey system theory can be applied to earthquake forecast. We introduce the above analysis methods and give a real example to evaluate and forecast. We also further discuss the problems of how to improve the precision of earthquake forecast and how to strengthen the forecast models in future research.

Application of Grey System Theory in Design For Cost (DFC)

Design For Cost (DFC) is a branch of Design For X (DFX). In this paper, wedefined DFC as a design method that analyzed and evaluated the product's life cycle cost (LCC), thenmodified the design to reduce the LCC. Nowadays it is a very difficult thing to obtain LCC data inChina or in developing countries. Statistical methods can not be used because available LCC data arefew. In order to solve this problem, we used grey system theory. Then relations of cost factorswere analyzed in LCC using grey relevant methods, and a GM(1,1) model between design parameters andLCC was established. Using this model, we can estimate and control LCC by changing design parametersat the beginning of the design stage.

DISCUSSION ON AVERAGE GENERATION OF TWODIMENSIONAL DATA SEQUENCE IN GREY SYSTEM THEORY

An unequal time interval sequence or a sequence with blanks is usually completed with average generation in grey system theory. This paper discovers that there exists obvious errors when using average generation to generate internal points of non-consecutive neighbours. The average generation and the preference generation of the sequence are discussed, the concave and convex properties show the status of local sequence and propose a new idea for using the status to build up the criteria of choosing generation coefficient. Compared with the general average method of the one-dimensional data sequence, the two-dimensional data sequence is defined and its average generation is discussed, and the coefficient decision method for the preference generation is presented.

Map Building for a Mobile Robot Based on Grey System Theory

In this paper, a new method for mobile robot map building based on grey system theory is presented, by which interpretation and integration of sonar readings can be solved robustly and efficiently. The conception of 'grey number is introduced to model and handle the uncertainty of sonar reading. A new data fusion approach based on grey system theory is proposed to construct environment model. Map building experiments are performed both on a platform of simulation and a real mobile robot. Experimental results show that our method is robust and accurate.

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.

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.

Analysis of aerobic granular sludge formation based on grey system theory

Based on grey entropy analysis, the relational grade of operational parameters with aerobic granular sludge＇s granulation indicators was studied. The former consisted of settling time （ST）, aeration time （AT）, superficial gas velocity （SGV）, height/diameter （H/D） ratio and organic loading rates （OLR）, the latter included sludge volume index （SVI） and set-up time. The calculated result showed that for SVI and set-up time, the influence orders and the corresponding grey entropy relational grades （GERG） were： SGV （0.9935） 〉 AT （0.9921） 〉 OLR （0.9894） 〉 ST （0.9876） 〉 H/D （0.9857） and SGV （0.9928） 〉 H/D （0.9914） 〉 AT （0.9909） 〉 OLR （0.9897） 〉 ST （0.9878）. The chosen parameters were all key impact factors as each GERG was larger than 0.98. SGV played an important role in improving SVI transformation and facilitating the set-up process. The influence of ST on SVI and set-up time was relatively low due to its dual functions. SVI transformation and rapid set-up demanded different optimal I-I/D ratio scopes （10-20 and 16-20）. Meanwhile, different functions could be obtained through adjusting certain factors＇ scope.

PROGRESS OF GREY SYSTEM MODELS

The progress of grey system models is reviewed, and the general grey numbers, the grey sequence op- erators and several most commonly used grey system models are introduced, such as the absolute degree of grey incidence model, the grey cluster model based on endpoint triangular whitenization functions, the grey cluster model based on center-point triangular whitenization functions, the grey prediction model of the model GM （ 1,1）, and the weighted multi-attribute grey target decision model.

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.

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.

Interval grey number sequence prediction by using non-homogenous exponential discrete grey forecasting model

This paper aims to study a new grey prediction approach and its solution for forecasting the main system variable whose accurate value could not be collected while the potential value set could be defined. Based on the traditional nonhomogenous discrete grey forecasting model（NDGM）, the interval grey number and its algebra operations are redefined and combined with the NDGM model to construct a new interval grey number sequence prediction approach. The solving principle of the model is analyzed, the new accuracy evaluation indices, i.e. mean absolute percentage error of mean value sequence（MAPEM） and mean percent of interval sequence simulating value set covered（MPSVSC）, are defined and, the procedure of the interval grey number sequence based the NDGM（IG-NDGM） is given out. Finally, a numerical case is used to test the modelling accuracy of the proposed model. Results show that the proposed approach could solve the interval grey number sequence prediction problem and it is much better than the traditional DGM（1,1） model and GM（1,1） model.

Expansion modelling of discrete grey model based on multi-factor information aggregation

This paper aims to study a novel expansion discrete grey forecasting model, which could aggregate input information more effectively. In general, existing multi-factor grey forecasting models, such as one order and h variables grey forecasting model （GM （1, h））, always aggregate the main system variable and independent variables in a linear form rather than a nonlinear form, while a nonlinear form could be used in more cases than the linear form. And the nonlinear form could aggregate collinear independent factors, which widely lie in many multi-factor forecasting problems. To overcome this problem, a new approach, named as the Solow residual method, is proposed to aggregate independent factors. And a new expansion model, feedback multi-factor discrete grey forecasting model based on the Solow residual method （abbreviated as FDGM （1, h））, is proposed accordingly. Then the feedback control equation and the parameters＇ solution of the FDGM （1, h） model are given. Finally, a real application is used to test the modelling accuracy of the FDGM （1, h） model. Results show that the FDGM （1, h） model is much better than the nonhomogeneous discrete grey forecasting model （NDGM） and the GM （1, h） model.

Application of the Grey topological method to predict the effects of ship pitching

Ship motion,with six degrees of freedom,is a complex stochastic process.Sea wind and waves are the primary influencing factors.Prediction of ship motion is significant for ship navigation.To eliminate errors,a path prediction model incorporating ship pitching was developed using the Gray topological method,after analyzing ship pitching motions.With the help of simple introduction to Gray system theory,we selected a group of threshold values.Based on an analysis of ship pitch angle sequences over 40 second intervals,a Grey metabolism GM(1,1) model was established according to the time-series which every threshold corresponded to.Forecasting future ship motion with the GM(1,1) model allowed drawing of the forecast curve with effective forecasting points.The precision of the test results show that the model is accurate,and the forecast results are reliable.