Grey New Information GOM(1,1) Model and its Application Based on Opposite-Direction Accumulated Generating and Background Value Optimization

Liao, De Gang; Luo, You Xin

doi:10.4028/www.scientific.net/AMR.321.33

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Grey New Information GOM(1,1) Model and its Application Based on Opposite-Direction Accumulated Generating and Background Value Optimization

Abstract:

The accuracy of the traditional grey model is not high for the monotonic decreasing data. The paper uses the opposite-direction accumulated method and makes full use of new information combined with optimization of the background values. It induces the formula of the parameters in the model and establishes the grey new information GOM(1,1) model in which it is based on the opposite-direction accumulated operation and optimization of the background values. It is a new method of the grey model. The examples show it is with higher accuracy than that of non-new information model.

Info:

Periodical:

Advanced Materials Research (Volume 321)

Main Theme:

Advanced Research on Intelligent Materials and Mechanical Engineering

Edited by:

Helen Zhang and David Jin

Pages:

33-36

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.321.33

Citation:

D. G. Liao and Y. X. Luo, "Grey New Information GOM(1,1) Model and its Application Based on Opposite-Direction Accumulated Generating and Background Value Optimization", Advanced Materials Research, Vol. 321, pp. 33-36, 2011

Online since:

August 2011

Authors:

De Gang Liao, You Xin Luo

Keywords:

Background Value, Data Processing, Grey Modeling, Grey Opposite-Direction Model, New Information, Opposite-Direction Accumulated Generating

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$38.00

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References

[1] You-xin, ZHANG Long-ting, LI Min. LI. Grey systems and applications in mechanical engineering(Changsha: National University of Defence Technology Press , 2001).

[2] SONG Zhong-min, DENG Ju-long.: Systems Engineering, Vol. 19, No. 1(1200), pp.66-69.

[3] Dang Y G, Liu S F, Chen K J. : Kybernetes, Vol. 33, No. 2(2004), p.247–254.

[4] LUO Y X, Xiao W Y. 2009 International Conference on Intelligent Computation Technology and Automation (ICICTA 2009), October10-11, 2009, Zhangjiajie, China, (2009), pp.120-123.

[5] Luo Y X, Che X Y.: 2009 Asia-Pacific Conference on Information Processing (APCIP 2009), Shenzhen, China，July 18-19, (2009), pp.37-40.

[6] WU Xiao, LUo You-xin，GUO Hui-xin, et al.: Machine Design and Manufacturing Engineering, , Vol. 30, No. 5(2001), pp.62-63.

Paper TitlePages

Using Grey Prediction Theory to Forecast Expenditure for Science & Technology Activities in China

Authors: Wu Wei Li

Abstract: Based on the statistical data during the period from 2003 to 2008 released by National Bureau of Statistics of China, this paper aims to predict the amount of expenditure for science & technology activities in China by the usage of GM (1,1) model with the six items. The results of this empirical study is that the grey prediction theory can fit the expenditure for science & technology activities amount development trend precisely in China. The accuracy of the prediction result is above 95% and corresponds with a distinction, which the grey theory can meet expectations with small samples or data. Research results show that this GM (1,1) model established in this paper could provide valuable information for policy makers in their efforts to make appropriate technological policies.

504

Application of Grey Model(1,1) in Road Traffic Accident Forecast

Authors: Xiao Kun Miao, Ming Yang Li

Abstract: Road traffic accident forecast is a complex stochastic process. Based on the statistics of road traffic accident, Grey Model (1, 1)( short for GM(1, 1)) was applied to forecast the future number of road traffic accident in this paper. GM(1, 1) was established according to the time-series. GM(1, 1) is usually applied for such stochastic unconfirmed problem as road traffic accident forecast. Base on MATLAB software, the forecast value of road traffic accident was given. The precision of the test results show that the model is accurate and the forecast results are reliable.

551

Grey New Information Unbiased GRM(1,1) Model Based on Accumulated Generating Operation in Reciprocal Number and its Application

Authors: W.Y. Xiao, Y.Y. Luo, Xiao Yi Che

Abstract: Monotonically decreasing sequence data for the traditional modeling method using the gray model accuracy is not high , and GM (1, 1) modeling method has inherent deviation , Model does not meet the compatibility condition, using Accumulated Generating Operation in reciprocal number ,make best use of last information and GM (1, 1) modeling , is deduced and the parameters optimization grey derivative calculation formula ,and then established GRM(1,1) based on accumulated generating operation in reciprocal number on the equidistance, Gray provides a new method of modeling . Data processing examples show that the model's practicality and reliability.

Grey Unbiased GOM(1,1) Model Based on Inverse Accumulated Generating Operation and its Application

Authors: You Xin Luo, De Gang Liao, Xiao Yi Che

Abstract: The accuracy of the traditional grey model is not high for the monotonic decreasing data and the model can not satisfy the compatibility conditions which make the error be not avoided. The paper adopts inverse accumulated generating operation and unbiased GM(1,1) model to induces the parameters in the model and establishes the unbiased grey unbiased GOM(1,1) model based on inverse accumulated generating operation. The model has high accuracy and has good value in the theory and practice. The examples show it is practical and reliable.

284

The Optimized White Differential Equation of GM(1,1) Based on the Original Grey Differential Equation

Authors: Rui Zhou, Jun Jie Li, Yao Chen

Abstract: This paper starting from the original grey differential equations, through finding the relationship between the raw data and the derivative of its , constructed a new white differential equation which equal to the original grey differential equation, at the same time, getting the new GM(1,1)model which closer to the changes of data. Through the modeling and prediction of the standard index series, this model not only adapts to low growth index series, but also adapts to high-growth index series, and the simulation accuracy and prediction accuracy are high.

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