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A novel fractional grey forecasting model with variable weighted buffer operator and its application in forecasting China's crude oil consumption

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Petroleum 2022年第2期8卷 139-157页

作者： Yong Wang Yuyang Zhang Rui Nie Pei Chi Xinbo He Lei Zhang School of Sciences Southwest Petroleum UniversityChengdu610500China

Oil is an important strategic material and civil *** prediction of oil consumption can provide basis for relevant departments to reasonably arrange crude oil production,oil import and export,and optimize the allocation of social ***,a new grey model FENBGM(1,1)is proposed to predict oil consumption in ***,the grey effect of the traditional GM(1,1)model was transformed into a quadratic *** different parameters were introduced to improve the accuracy of the model,and the new initial conditions were designed by optimizing the initial values by weighted buffer *** with the reprocessing of the original data,the scheme eliminates the random disturbance effect,improves the stability of the system sequence,and can effectively extract the potential pattern of future ***,the cumulative order of the new model was optimized by fractional cumulative generation *** the same time,the smoothness rate quasi-smoothness condition was introduced to verify the stability of the model,and the particle swarm optimization algorithm(PSO)was used to search the optimal parameters of the model to enhance the adaptability of the *** on the above improvements,the new combination prediction model overcomes the limitation of the traditional grey model and obtains more accurate and robust prediction ***,taking the petroleum consumption of China's manufacturing industry and transportation,storage and postal industry as an example,this paper verifies the validity of FENBGM(1,1)model,analyzes and forecasts China's crude oil consumption with several commonly used forecasting models,and uses FENBGM(1,1)model to forecast China's oil consumption in the next four *** results show that FENBGM(1,1)model performs best in all ***,based on the prediction results of FENBGM(1,1)model,some reasonable suggestions are put forward for China's oil consumption planning.

关键词： grey forecasting model Variable weighted buffer operator Particle swarm optimization Oil consumption forecast

A new grey forecasting model based on BP neural network and Markov chain

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Journal of Central South University of Technology 2007年第5期14卷 713-718页

作者：李存斌王恪铖 School of Business Administration North China Electric Power University

A new grey forecasting model based on BP neural network and Markov chain was proposed. In order to combine the grey forecasting model with neural network, an important theorem that the grey differential equation is equivalent to the time response model, was proved by analyzing the features of grey forecasting model（GM（1,1））. Based on this, the differential equation parameters were included in the network when the BP neural network was constructed, and the neural network was trained by extracting samples from grey system＇s known data. When BP network was converged, the whitened grey differential equation parameters were extracted and then the grey neural network forecasting model （GNNM（1,1）） was built. In order to reduce stochastic phenomenon in GNNM（1,1）, the state transition probability between two states was defined and the Markov transition matrix was established by building the residual sequences between grey forecasting and actual value. Thus, the new grey forecasting model（MNNGM（1,1）） was proposed by combining Markov chain with GNNM（1,1）. Based on the above discussion, three different approaches were put forward for forecasting China electricity demands. By comparing GM（1, 1） and GNNM（1,1） with the proposed model, the results indicate that the absolute mean error of MNNGM（1,1） is about 0.4 times of GNNM（1,1） and 0.2 times of GM（I, 1）, and the mean square error of MNNGM（1,1） is about 0.25 times of GNNM（1,1） and 0.1 times of GM（1,1）.

关键词： grey forecasting model neural network Markov chain electricity demand forecasting

grey relationship analysis and grey forecasting modeling on thermal stability of synthetic single diamond

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Journal of Harbin Institute of Technology(New Series) 2006年第1期13卷 73-78页

作者：王适张弘弢董海 Key Laboratory of Precision & Non-traditional Machining of Ministry of Education Dalian University of Technology Dalian 116024 China

Through analyzing 7 Ib-type samples of synthetic single diamonds by their DTA and TG in air, we ascertained the extrapolated onset temperature on the curves of DTA as the characteristic temperature of their thermal stabilities. Based on the grey system theory, we analyzed 4 factors influential in the thermal stability by the grey relationship analysis, a quantitative method, and derived the grey relationship sequence, that is, the rank of the influence extent of 4 factors on the thermal stability. Furthermore, we established the grey forecasting model, namely GM(1,5), for predicting the thermal stability of single diamonds with their intrinsic properties, which was then examined by a deviation-probability examination. The results illustrate that it is reasonable to take the Extrapolated Onset Temperature in DTA as the characteristic temperature for thermal stability (TS) of Ib-type synthetic single diamonds. The nitrogen content and grain shape regularity of diamonds are dominating factors. Likewise, grain size and compressive strength are minor factors. In addition, GM(1,5) can be used to predict the thermal stability of Ib-type synthetic single diamonds available. The precision rank of GM(1,5) is ‘GOOD’.

关键词： synthetic single diamond thermal stability grey relationship analysis grey forecasting model