scholarly journals A new grey quadratic polynomial model and its application in the COVID-19 in China

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jianbo Zhang ◽  
Zeyou Jiang
Keyword(s):  
Prediction Models ◽  
Early Stage ◽  
Quadratic Polynomial ◽  
Grey Model ◽  
Grey Prediction ◽  
New Model ◽  
Polynomial Term ◽  
Death Cases ◽  
Analytical Expressions ◽  
Model Technique

AbstractThis paper develops a new grey prediction model with quadratic polynomial term. Analytical expressions of the time response function and the restored values of the new model are derived by using grey model technique and mathematical tools. With observations of the confirmed cases, the death cases and the recovered cases from COVID-19 in China at the early stage, the proposed forecasting model is developed. The computational results demonstrate that the new model has higher precision than the other existing prediction models, which show the grey model has high accuracy in the forecasting of COVID-19.

scholarly journals An Improved Nonequidistant Grey Model Based on Simpson Formula and Its Application

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhiming Hu ◽  
Chong Liu
Keyword(s):  
Prediction Accuracy ◽  
Prediction Models ◽  
Prediction Performance ◽  
Grey System Theory ◽  
Grey Model ◽  
Grey Prediction ◽  
Grey System ◽  
Proposed Model ◽  
Grey Models ◽  
High Prediction

Grey prediction models have been widely used in various fields of society due to their high prediction accuracy; accordingly, there exists a vast majority of grey models for equidistant sequences; however, limited research is focusing on nonequidistant sequence. The development of nonequidistant grey prediction models is very slow due to their complex modeling mechanism. In order to further expand the grey system theory, a new nonequidistant grey prediction model is established in this paper. To further improve the prediction accuracy of the NEGM (1, 1, t2) model, the background values of the improved nonequidistant grey model are optimized based on Simpson formula, which is abbreviated as INEGM (1, 1, t2). Meanwhile, to verify the validity of the proposed model, this model is applied in two real-world cases in comparison with three other benchmark models, and the modeling results are evaluated through several commonly used indicators. The results of two cases show that the INEGM (1, 1, t2) model has the best prediction performance among these competitive models.

scholarly journals Forecasting Chinese Wind Power Installed Capacity Using a Novel Grey Model with Parameters Combination Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Xiaoshuang Luo ◽  
Bo Zeng ◽  
Hui Li ◽  
Wenhao Zhou
Keyword(s):  
Prediction Model ◽  
Wind Power ◽  
Prediction Models ◽  
Grey Model ◽  
The Novel ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Combination Optimization ◽  
Installed Capacity ◽  
Novel Model

The intermittent and uncertain characteristics of wind generation have brought new challenges for the hosting capacity and the integration of large-scale wind power into the power system. Consequently, reasonable forecasting wind power installed capacity (WPIC) is the most effective and applicable solution to meet this challenge. However, the single parameter optimization of the conventional grey model has some limitations in improving its modeling ability. To this end, a novel grey prediction model with parameters combination optimization is proposed in this paper. Firstly, considering the modeling mechanism and process, the order of accumulation generation of the grey prediction model is optimized by Particle Swarm Optimization (PSO) Algorithm. Secondly, as different orders of accumulation generation correspond to different parameter matrixes, the background value coefficient of the grey prediction model is optimized based on the optimal accumulation order. Finally, the novel model of combinational optimization is employed to simulate and forecast Chinese WPIC, and the comprehensive error of the novel model is only 1.34%, which is superior to the other three grey prediction models (2.82%, 1.68%, and 2.60%, respectively). The forecast shows that China’s WPIC will keep growing in the next five years, and some reasonable suggestions are put forward from the standpoint of the practitioners and governments.

scholarly journals Research on a Grey Prediction Model of Population Growth Based on a Logistic Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Mingyu Tong ◽  
Zou Yan ◽  
Liu Chao
Keyword(s):  
Population Growth ◽  
Prediction Model ◽  
Growth Model ◽  
Prediction Models ◽  
Least Squares Method ◽  
Grey Prediction ◽  
Growth Prediction ◽  
New Model ◽  
Grey Prediction Model ◽  
Advantages And Disadvantages

The classical population growth models include the Malthus population growth model and the logistic population growth model, each of which has its advantages and disadvantages. To address the disadvantages of the two models, this paper establishes a grey logistic population growth prediction model, based on the modeling mechanism of the grey prediction model and the characteristics of the logistic model, which uses the least-squares method to estimate the maximum population capacity. In accordance with the data characteristics of population growth, the weakening buffer operator is used to establish the weakening buffer operator grey logistic population growth prediction model, which improves its accuracy, thus improving the classic population prediction model. Four actual case datasets are used simultaneously, and the two classical grey prediction models are compared. The results of the six evaluation indicators show that the effects of the new model demonstrate obvious advantages. Finally, the new model is applied to the population forecast of Chongqing, China. The prediction results suggest that the population may reach a peak in 2020 and decline in the future. This finding is consistent with the logistic population growth model.

Comparison of Two Grey Models Applied in Predicting Numbers of Candidates for College Entrance Examination

2011 ◽  
Vol 105-107 ◽  
pp. 2225-2228
Author(s):  
Gang Li ◽  
Ying Fang ◽  
Ya La Tong
Keyword(s):  
Decision Making ◽  
Prediction Models ◽  
Technical Information ◽  
Grey Model ◽  
Entrance Examination ◽  
Grey Prediction ◽  
College Entrance Examination ◽  
College Entrance ◽  
Grey Models ◽  
The Difference

This paper mainly used two different grey models to predict the numbers of candidates of applying for the college entrance examination. We firstly introduced the conception of GM (1,1,D) and GM (1,1,C), briefly explained the difference between them from the aspect of theory, and then put them into the application of predicting the situation of variance of candidates for college entrance examination. Finally, we analyzed the reasons for this change and countermeasures. Compared to the application of numbers of candidates for college entrance examination by using two grey prediction models, this paper gives an effective method of data analysis, and provides technical information for the relevant decision-making departments. This paper set a good example for the application of selecting the correct grey model to address specific problems.朗读 显示对应的拉丁字符的拼音 字典

scholarly journals Forecasting China’s per Capita Living Energy Consumption by Employing a Novel DGM (1, 1, tα) Model with Fractional Order Accumulation

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yumu Lu ◽  
Chong Liu ◽  
Haodan Pang ◽  
Ting Feng ◽  
Zijie Dong
Keyword(s):  
Energy Consumption ◽  
Fractional Order ◽  
Prediction Models ◽  
Grey Model ◽  
Grey Prediction ◽  
Grey System ◽  
Annual Increment ◽  
Model Based ◽  
Proposed Model ◽  
Per Capita

The living energy consumption of residents has become an important technical index to promote the economic and social development strategy. The country’s medium- and short-term living energy consumption is featured with both a certainty of annual increment and an uncertainty of random variation. Thus, it can be seen as a typical grey system and shall be suitable for the grey prediction model. In order to explore the future development trend of China’s per capita living energy consumption, this paper establishes a novel grey model based on the discrete grey model with time power term and the fractional accumulation (FDGM (1, 1, tα) for short) for forecasting China’s per capita living energy consumption, which makes the existing model to adapt to different time series by adjusting fractional order accumulation parameter and power term. In order to verify the feasibility and effectiveness of the novel model, the proposed and eight other existing grey prediction models are applied to the case of China’s per capita living energy consumption. The results show that the proposed model is more suitable for predicting China’s per capita energy consumption than the other eight grey prediction models. Finally, the proposed model based on metabolism mechanism is used to predict China’s per capita living energy consumption from 2018 to 2029, which can provide a reference for energy companies or government decision makers.

scholarly journals Research on a Novel Kernel Based Grey Prediction Model and Its Applications

2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
Xin Ma
Keyword(s):  
Prediction Models ◽  
Kernel Method ◽  
Nonlinear Function ◽  
Grey Model ◽  
Production Data ◽  
World Production ◽  
Grey Prediction ◽  
Gas Well ◽  
Exponential Time ◽  
Well Production

The discrete grey prediction models have attracted considerable interest of research due to its effectiveness to improve the modelling accuracy of the traditional grey prediction models. The autoregressive GM(1,1)model, abbreviated as ARGM(1,1), is a novel discrete grey model which is easy to use and accurate in prediction of approximate nonhomogeneous exponential time series. However, the ARGM(1,1)is essentially a linear model; thus, its applicability is still limited. In this paper a novel kernel based ARGM(1,1)model is proposed, abbreviated as KARGM(1,1). The KARGM(1,1)has a nonlinear function which can be expressed by a kernel function using the kernel method, and its modelling procedures are presented in details. Two case studies of predicting the monthly gas well production are carried out with the real world production data. The results of KARGM(1,1)model are compared to the existing discrete univariate grey prediction models, including ARGM(1,1), NDGM(1,1,k), DGM(1,1), and NGBMOP, and it is shown that the KARGM(1,1)outperforms the other four models.

Development of standardized regression-based formulas to assess meaningful cognitive change in early Parkinson’s disease

2020 ◽  
Author(s):  
Hannah L Combs ◽  
Kate A Wyman-Chick ◽  
Lauren O Erickson ◽  
Michele K York
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Test Scores ◽  
Prediction Models ◽  
Early Stage ◽  
Cognitive Change ◽  
Cognitive Test ◽  
Healthy Controls ◽  
Change Over Time ◽  
Over Time

Abstract Objective Longitudinal assessment of cognitive and emotional functioning in patients with Parkinson’s disease (PD) is helpful in tracking progression of the disease, developing treatment plans, evaluating outcomes, and educating patients and families. Determining whether change over time is meaningful in neurodegenerative conditions, such as PD, can be difficult as repeat assessment of neuropsychological functioning is impacted by factors outside of cognitive change. Regression-based prediction formulas are one method by which clinicians and researchers can determine whether an observed change is meaningful. The purpose of the current study was to develop and validate regression-based prediction models of cognitive and emotional test scores for participants with early-stage idiopathic PD and healthy controls (HC) enrolled in the Parkinson’s Progression Markers Initiative (PPMI). Methods Participants with de novo PD and HC were identified retrospectively from the PPMI archival database. Data from baseline testing and 12-month follow-up were utilized in this study. In total, 688 total participants were included in the present study (NPD = 508; NHC = 185). Subjects from both groups were randomly divided into development (70%) and validation (30%) subsets. Results Early-stage idiopathic PD patients and healthy controls were similar at baseline. Regression-based models were developed for all cognitive and self-report mood measures within both populations. Within the validation subset, the predicted and observed cognitive test scores did not significantly differ, except for semantic fluency. Conclusions The prediction models can serve as useful tools for researchers and clinicians to study clinically meaningful cognitive and mood change over time in PD.

Phase Separation: From the Initial Nucleation Stage to the Final Ostwald Ripening Regime

1997 ◽  
Vol 481 ◽  
Author(s):  
Celeste Sagui ◽  
Dean Stinson O'Gorman ◽  
Martin Grant
Keyword(s):  
Phase Separation ◽  
Late Stage ◽  
Ostwald Ripening ◽  
Early Stage ◽  
Classical Problem ◽  
Nucleation And Growth ◽  
New Model ◽  
Nucleation Stage ◽  
Initial Nucleation

ABSTRACTIn this work we have re-examined the classical problem of nucleation and growth. A new model considers the correlations among droplets and naturally incorporates the crossover from the early-stage, nucleation dominated regime to the scaling, late-stage, coarsening regime within a single framework.

Grey prediction models for the standard limit of vehicle noise

2017 ◽  
Vol 232 (7) ◽  
pp. 973-979 ◽  
Author(s):  
Zhendong Zhao ◽  
Changzheng Hu
Keyword(s):  
Prediction Models ◽  
System Development ◽  
Original Data ◽  
Grey Theory ◽  
Grey Prediction ◽  
Vehicle Noise ◽  
Direct Model ◽  
The Future ◽  
Quadratic Fitting ◽  
Verhulst Model

With an increasing number of vehicles and increasing environmental protection requirements, countries have accelerated the rate of revision of automobile noise standards and legislation. Scientific prediction of the limiting values in future noise standards is helpful to promote the development of automobile noise reduction technology and measurement analysis technology. The development of noise standard limits has its own objective laws and is restricted to the current and future developments in automotive technology. The amplitude of noise will be reduced increasingly less in the future. Grey prediction theory can explore the variation rules by processing a few effective data. In this paper, grey theory is used to deal with the limited original data in the vehicle noise standard. Non-equal-interval quadratic fitting of the grey Verhulst direct model to predict the future noise standard limits is selected on the basis of calculation and comparison of different models. The Verhulst model is employed to describe the system development by using the characteristics of saturation. By means of quadratic fitting, the accuracy of the Verhulst model can be further improved. The simulation results show the validity and the accuracy of the model. The prediction result is useful for standards and regulations makers and for car manufacturers.

scholarly journals Forecasting the Number of New Coronavirus Infections Us-ing an Improved Grey Prediction Model

2021 ◽  
Author(s):  
Hui Li ◽  
Bo Zeng ◽  
Jianzhou Wang ◽  
Hua’an Wu
Keyword(s):  
Prediction Model ◽  
Particle Swarm Optimization Algorithm ◽  
Percentage Error ◽  
Small Data ◽  
Grey Prediction ◽  
Matlab Simulation ◽  
Short Term ◽  
New Model ◽  
Grey Prediction Model ◽  
Coronavirus Infections

Background: Recently, a new coronavirus has been rapidly spreading from Wuhan, China. Forecasting the number of infections scientifically and effectively is of great significance to the allocation of medical resources and the improvement of rescue efficiency. Methods: The number of new coronavirus infections was characterized by “small data, poor information” in the short term. The grey prediction model provides an effective method to study the prediction problem of “small data, poor information”. Based on the order optimization of NHGM(1,1,k), this paper uses particle swarm optimization algorithm to optimize the background value, and obtains a new improved grey prediction model called GM(1,1|r,c,u). Results: Through MATLAB simulation, the comprehensive percentage error of GM(1,1|r,c,u), NHGM(1,1,k), UGM(1,1), DGM(1,1) are 2.4440%, 11.7372%, 11.6882% and 59.9265% respectively, so the new model has the best prediction performance. The new coronavirus infections was predicted by the new model. Conclusion: The number of new coronavirus infections in China increased continuously in the next two weeks, and the final infections was nearly 100 thousand. Based on the prediction results, this paper puts for-ward specific suggestions.

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