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.B...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.展开更多
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 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.展开更多
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...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.展开更多
Objective:To explore the feasibility of using grey model GM(1,1)model to predict syphilis,and to provide a theoretical basis for the health sector to develop corresponding strategies.Methods:GM(1,1)model was used to c...Objective:To explore the feasibility of using grey model GM(1,1)model to predict syphilis,and to provide a theoretical basis for the health sector to develop corresponding strategies.Methods:GM(1,1)model was used to construct and simulate the incident rate and case number of syphilis in China from 2009 to 2018 to predict the change trend.Results:The GM(1,1)prediction model of syphilis incident rate was x^(1)(k+1)=929.367901 e(0.029413k)-906.297901.The GM(1,1)prediction model for the number of syphilis patients was x^(1)(k+1)=1060.278025 e(0.034280k)-1029.639925.For syphilis incidence model,the posterior difference ratio was 0.19819 and the probability of small error was 1.For the syphilis incident number model,the posterior difference ratio was 0.18450 and the probability of small error was 1.The above models have good fitting accuracy with excellent grade level and can be predicted by extrapolation and predicted that the syphilis incidence in 2019-2021 may be 36.15 per 100,000,37.23 per 100,000 and 38.34 per 100,000,respectively.From 2019 to 2021,the number of incident syphilis cases in China may be 503,406,520,962 and 539,130,respectively.Conclusion:The GM(1,1)model can well fit and predict the change trend of syphilis incidence in time series.The prediction model showed that the incidence of syphilis may continue to increase and the number of syphilis cases per year may continue to increase substantially.More effort is needed to strengthen the prevention and treatment of venereal disease,reduce venereal harm to the population and improve the early detection rate of syphilis.展开更多
In order to realize the accurate prediction of the total output value of construction industry in the future,the grey prediction model is used to compare the measured value with the predicted value from 2012 to 2021,a...In order to realize the accurate prediction of the total output value of construction industry in the future,the grey prediction model is used to compare the measured value with the predicted value from 2012 to 2021,and based on the existing data,the total output value of construction industry in Jiangxi Province in the next five years is predicted.The results show that the grey prediction model has a good prediction effect,and the error between the predicted value and the measured value is within 14%,which provides a basis for policy adjustment and resource optimization.展开更多
Based on the optimization method, a new modified GM (1,1) model is presented, which is characterized by more accuracy prediction for the grey modeling.
基金supported by the National Natural Science Foundation of China(7084001290924022)the Ph.D.Thesis Innovation and Excellent Foundation of Nanjing University of Aeronautics and Astronautics(2010)
文摘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.
基金Project supported by the National Natural Science Foundation of China(No.50574099)the National Science Foundation for Innovative Research Group(No.50321402)and the Natural Science Foundation of Hunan Province(No.06JJ30024)
文摘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.
文摘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.
文摘Objective:To explore the feasibility of using grey model GM(1,1)model to predict syphilis,and to provide a theoretical basis for the health sector to develop corresponding strategies.Methods:GM(1,1)model was used to construct and simulate the incident rate and case number of syphilis in China from 2009 to 2018 to predict the change trend.Results:The GM(1,1)prediction model of syphilis incident rate was x^(1)(k+1)=929.367901 e(0.029413k)-906.297901.The GM(1,1)prediction model for the number of syphilis patients was x^(1)(k+1)=1060.278025 e(0.034280k)-1029.639925.For syphilis incidence model,the posterior difference ratio was 0.19819 and the probability of small error was 1.For the syphilis incident number model,the posterior difference ratio was 0.18450 and the probability of small error was 1.The above models have good fitting accuracy with excellent grade level and can be predicted by extrapolation and predicted that the syphilis incidence in 2019-2021 may be 36.15 per 100,000,37.23 per 100,000 and 38.34 per 100,000,respectively.From 2019 to 2021,the number of incident syphilis cases in China may be 503,406,520,962 and 539,130,respectively.Conclusion:The GM(1,1)model can well fit and predict the change trend of syphilis incidence in time series.The prediction model showed that the incidence of syphilis may continue to increase and the number of syphilis cases per year may continue to increase substantially.More effort is needed to strengthen the prevention and treatment of venereal disease,reduce venereal harm to the population and improve the early detection rate of syphilis.
文摘In order to realize the accurate prediction of the total output value of construction industry in the future,the grey prediction model is used to compare the measured value with the predicted value from 2012 to 2021,and based on the existing data,the total output value of construction industry in Jiangxi Province in the next five years is predicted.The results show that the grey prediction model has a good prediction effect,and the error between the predicted value and the measured value is within 14%,which provides a basis for policy adjustment and resource optimization.
文摘Based on the optimization method, a new modified GM (1,1) model is presented, which is characterized by more accuracy prediction for the grey modeling.
