A combination method of optimization of the back-ground value and optimization of the initial item is proposed. The sequences of the unbiased exponential distribution are simulated and predicted through the optimizati...A combination method of optimization of the back-ground 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 ful y expressed through the improvement of the initial item in the proposed time response function. A numerical example is employed to il ustrate 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.展开更多
In order to improve prediction accuracy of the grey prediction model and forecast China energy consumption and production in a short term, this paper proposes a novel com- prehensively optimized GM（1,1） model, also ...In order to improve prediction accuracy of the grey prediction model and forecast China energy consumption and production in a short term, this paper proposes a novel com- prehensively optimized GM（1,1） model, also named COGM（1,1）, based on the grey modeling mechanism. First, the relationship of the background value formula and its whitenization equation is analyzed and a new method optimizing background values is proposed to eliminate systemic errors in the modeling process. Second, the solving process of the new model is derived. For parameter estimation, a set of auxiliary parameters are used to change grey equation＇s form. Then, original parameters are re- stored by an equations system. After solving the whitenization equation, initial value in time response function is established by least errors criteria. Finally, a numerical case and comparison with other grey prediction models are made to testify the new model＇s effectiveness, and the computational results show that the COGM（1,1） model has a better property and achieves higher precision. The new model is used to forecast China energy con- sumption and production, and the ability of energy self-sufficiency is further analyzed. Results indicate that gaps between consump- tion and production in future are predicted to decline.展开更多
基金supported by the Key Project of National Social Science Foundation(12AZD111)the National Project for Education Science Planning(EFA110351)+2 种基金the Humanities and Social Science Foundation of Ministry of Education of China(12YJCZH207)the Key Project for Jiangsu Province Social Science Foundation(12DDA011)the Jiangsu College of Humanities and Social Sciences outside Campus Research Base:Chinese Development of Strategic Research Base for Internet of Things
文摘A combination method of optimization of the back-ground 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 ful y expressed through the improvement of the initial item in the proposed time response function. A numerical example is employed to il ustrate 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.
基金supported by the National Natural Science Foundation of China(710710777130106071371098)
文摘In order to improve prediction accuracy of the grey prediction model and forecast China energy consumption and production in a short term, this paper proposes a novel com- prehensively optimized GM（1,1） model, also named COGM（1,1）, based on the grey modeling mechanism. First, the relationship of the background value formula and its whitenization equation is analyzed and a new method optimizing background values is proposed to eliminate systemic errors in the modeling process. Second, the solving process of the new model is derived. For parameter estimation, a set of auxiliary parameters are used to change grey equation＇s form. Then, original parameters are re- stored by an equations system. After solving the whitenization equation, initial value in time response function is established by least errors criteria. Finally, a numerical case and comparison with other grey prediction models are made to testify the new model＇s effectiveness, and the computational results show that the COGM（1,1） model has a better property and achieves higher precision. The new model is used to forecast China energy con- sumption and production, and the ability of energy self-sufficiency is further analyzed. Results indicate that gaps between consump- tion and production in future are predicted to decline.
