Grey optimized models and their existence theorem

Tong, Xiaojun; Mian-yun, Chen; Li, Zhijun

doi:10.1108/03684920410514373

Cited by 2 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wang et al presented an optimization method of the background value based on discrete data and obtained effective prediction results [6]. Tong et al proposed a new method of constructing the background value based on ideal absolute errors and relative errors and illustrated that this new model has good prediction results [7]. The other is optimization of the grey derivative [8,9].…”

Section: Introductionmentioning

confidence: 99%

Optimization approach of background value and initial item for improving prediction precision of GM(1,1) model

Wang,

Liu,

Tang

et al. 2014

J. of Syst. Eng. Electron.

View full text Add to dashboard Cite

A combination method of optimization of the background value and optimization of the initial item is proposed. The sequences of the unbiased exponential distribution are simulated and predicted through the optimization of the background value in grey differential equations. The principle of the new information priority in the grey system theory and the rationality of the initial item in the original GM(1,1) model are fully expressed through the improvement of the initial item in the proposed time response function. A numerical example is employed to illustrate that the proposed method is able to simulate and predict sequences of raw data with the unbiased exponential distribution and has better simulation performance and prediction precision than the original GM(1,1) model relatively.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimization approach of background value and initial item for improving prediction precision of GM(1,1) model

Wang,

Liu,

Tang

et al. 2014

J. of Syst. Eng. Electron.

View full text Add to dashboard Cite

show abstract

“…Markov chain is a kind of special Markov process. Through researching the initial probability of different states and transition probability between states, Markov chain can predict future state of the event (Mian‐Yun et al , 2004; Xiaojun et al , 2004).…”

Section: Introductionmentioning

confidence: 99%

Improved grey‐Markov chain algorithm for forecasting

Wang

Mian-yun

2009

Kybernetes

Self Cite

View full text Add to dashboard Cite

PurposeThe purpose of this paper is to present a method to accurately forecast the tendency of the gross amount of energy sources consumption of the country and construct a new kind of algorithm for forecasting that synthesizes the advantages of the grey model, Markov chains, and least square method.Design/methodology/approachWith the application of this new algorithm, this paper have forecasted the trend of the gross amount of energy sources consumption of the country and come to the conclusions that the new algorithm is more precise than the grey model. It is proved that the improved grey‐Markov chain algorithm is effective and can be used by authorities to make decision.FindingsIt was found that combining the grey model, Markov chains, and least square method, can be a new algorithm for forecasting the trendency of the gross amount of energy sources consumption.Research limitations/implicationsThe new algorithm is only suitable for the short‐term forecast.Originality/valueThe grey forecasting method reflects the overall tendency of primitive data sequence of the gross amount of energy source, and the Markov chain forecasting method reflects the effect of the random fluctuation. The least square method reflects the tendency of increase. The new algorithm is more precise than the grey model.

show abstract

Grey optimized models and their existence theorem

Cited by 2 publications

References 2 publications

Optimization approach of background value and initial item for improving prediction precision of GM(1,1) model

Optimization approach of background value and initial item for improving prediction precision of GM(1,1) model

Improved grey‐Markov chain algorithm for forecasting

Contact Info

Product

Resources

About