Non-Equidistant Grey Model Based on Background Value and Initial Condition Optimization and Its Application
Authors: 
Ziheng Wu, Yang Liu, Cong LiAuthors Info & Claims
Ziheng Wu, Yang Liu, Cong LiAuthors Info & ClaimsArticle No.: 23, Pages 1 - 4
Abstract
As the background value and the initial condition are important factors affecting the precision of grey system model, in this paper, we put forward an improved non-equidistant GM(1,1) model based on PSO according to the practical application need, in which the background value is optimized firstly and a new initial condition is presented based on the principle of new information priority. Under the algorithm of minimizing the square sum of the relative error between the original series and the forecasting sequences, the solution to the optimized time parameter is given, the particle swarm optimization (PSO) algorithm is used as a tool to optimize the parameter in the background value and the initial condition. The experimental result shows the effectiveness and applicability of the proposed non-equidistant GM(1,1) model.
References
[1]
Deng JL (1982). Control problems of grey systems [J]. Systems and Control Letters. 1 (5): 288-294.
[2]
Deng JL (2002). Foundation of grey theory [M]. Wuhan: Huazhong University of Science and Technology Press, 23-25.
[3]
Liu SF, Dang YG, Fang ZG (2004). Grey system theory and its application [M]. Beijing: Science Press, 1-8.
[4]
Ding S, Keith H, Dang Y G (2018). Forecasting China's electricity consumption using a new grey prediction model [J]. Energy, 149: 314-328.
[5]
Ding S, Dang YG, Xu N, Wei L, Ye J (2017). Multi-variable time delayed discrete grey model [J]. Control and Decision, 32(11): 1997-2004.
[6]
Li D C, Chang C J, Chen W C, (2011). An extended grey forecasting model for omnidirectional forecasting considering data gap difference[J]. Applied Mathematical Modelling, 35(10): 5051-5058.
[7]
Wu BB, Chen L, Ge C (2012). Application of improved non-equidistance GM(1,1) model in dam settlement analysis [J]. Water Resources and Power. 30 (6): 95-97.
[8]
Wang ZX, Dang YG, Liu SF (2012). Non-equidistant GM(1,1) power model and its application in engineering [J]. Strategic Study of CAE, 14(7): 98-102.
[9]
Zheng B H, Luo L (2015). Non-equidistance optimum grey model GM (1,1) of requirement analysis of urban construction land and its empirical study[J]. Electronic Journal of Geotechnical Engineering, 20 (22): 1227-1232.
[10]
Guo H, Xiao XP, Jeffrey F (2015). Non-equidistance GM(1,1,tα) model with time power and its application [J]. Control and Decision, 30(8): 1514-1518.
[11]
Shen Y, He B, Qin P (2016). Fractional-order grey prediction method for non-equidistant sequences[J]. Entropy, 18(6): 227-243.
[12]
Luo YX (2010). Non-equidistant step by step optimum new information GM(1,1) and its application [J]. Systems Engineering-Theory & Practice, 30(12): 2254-2258.
[13]
Xiong PP, Dang YG, Yao TX (2015). Modeling method of non-equidistant GM(1,1) model based on optimization initial condition [J]. Control and Decision, 30(11): 2097-2102.
[14]
Xi L, Ding S, Xu N, Xiong PP (2019). Research on optimization of non-equidistant GM(1,1) model based on the principle of new information priority [J]. Control and Decision, 34(10): 2221-2228.
[15]
Jiang SQ, Liu SF, Zhou XC (2014). Optimization of background value in GM(1,1) based on compound trapezoid formula [J]. Control and Decision, 29(12): 2221-2225.
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October 2021
660 pages
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Association for Computing Machinery
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Published: 07 December 2021
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- Research-article
- Research
- Refereed limited
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- natural science foundation Anhui Province
- Opening Project of Key Laboratory of Power Electronics and Motion Control of Anhui Higher Education Institutions
- science and technology projects of Xuancheng
- opening project of Anhui province key laboratory of special and heavy load robot
- science foundation for young scientists of Anhui university of technology
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CSAE 2021
CSAE 2021: The 5th International Conference on Computer Science and Application Engineering
October 19 - 21, 2021
Sanya, China
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