Based on the reason that the traditional buffer operator cannot adjust the action intensity, this paper proposes a positive real order weakening buffer operator, which solves the disadvantage that the original operator cannot be fine-tuned, and is more suitable for real life systems. By defining positive real order weakening buffer operator and according to the combination number and the nature of gamma function, the two are connected, and the positive real order weakening buffer sequence is transformed by gamma function. Next a quadratic time-varying linear parameter grey discrete prediction model (QTDGM) is established by using the constructed positive real order weakening buffer operator. The iterative optimization method of simulation base value is given, and the optimization model is established and the solution algorithm is proposed. Finally, the steps of modeling and forecasting by using QDGM model are described. In the case of science popularization fund forecast and raw coal output forecast, QTDGM model shows superior prediction effect. The relative error of the model is 0.34% ~ 7% in the three cases, which is much lower than that of the model using integer order weakening buffer operator and also lower than that of the linear time-varying parameter grey discrete model. QTDGM is more suitable for complex sample systems.
| Published in | American Journal of Information Science and Technology (Volume 2, Issue 3) |
| DOI | 10.11648/j.ajist.20180203.12 |
| Page(s) | 74-82 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Grey System, Fractional Order Buffer Operator, Qtdgm, Iteration and Optimization
| [1] | Ju-Long D. Control problem of grey systems [J]. Systems & Control Letters, 1982, 1(5): 288-294. |
| [2] | Deng J L. The grey exponential law for accumulative generation—the problem of optimal processing of information in grey control systems. [J]. J. huazhong Univ.sci.tech, 1987(5): 7-12. |
| [3] | Li-Yun W U, Zheng-Peng W U, Mei L I, et al. Quadratic time-varying parameters discrete grey model [J]. Systems Engineering-Theory & Practice, 2013, 33(11): 2887-2893. |
| [4] | Pan X J, Wei Z, Tian Z, et al. Fractional Order Discrete Grey Model and Its Application in Spare Parts Demand Forecasting [J]. Acta Armamentarii, 2017, 38(4): 785-792. |
| [5] | Wu L, Liu S, Yao L, et al. Grey system model with the fractional order accumulation [J]. Communications in Nonlinear Science & Numerical Simulation, 2013, 18(7): 1775-1785. |
| [6] | Laurinčikas A, Garunkštis R. Euler Gamma-Function [M]// The Lerch Zeta-function. Springer Netherlands, 2003: 1-15. |
| [7] | Wu L, Qi Y, Wu Z. Grey Discrete time-varying Model and Its application [C]// IEEE International Conference on Grey Systems and Intelligent Services. IEEE, 2015: 242-246. |
| [8] | Xie N, Liu S. Research on discrete grey model and its mechanism [C]// IEEE International Conference on Systems, Man and Cybernetics. IEEE, 2006: 606-610 Vol. 1. |
| [9] | Zhang S J, Chen S Y, Transportation S O, et al. Optimization of GM (1, 1) Power Model and Its Application [J]. Systems Engineering, 2016. |
| [10] | Xie N M, Liu S F. Discrete grey forecasting model and its optimization [J]. Applied Mathematical Modeling, 2009, 32(2): 1173-1186. |
APA Style
Mengdi Jin, Jiwei Liu. (2018). Further Promotion of Quadratic Time-Varying Parameters Discrete Grey Model. American Journal of Information Science and Technology, 2(3), 74-82. https://doi.org/10.11648/j.ajist.20180203.12
ACS Style
Mengdi Jin; Jiwei Liu. Further Promotion of Quadratic Time-Varying Parameters Discrete Grey Model. Am. J. Inf. Sci. Technol. 2018, 2(3), 74-82. doi: 10.11648/j.ajist.20180203.12
AMA Style
Mengdi Jin, Jiwei Liu. Further Promotion of Quadratic Time-Varying Parameters Discrete Grey Model. Am J Inf Sci Technol. 2018;2(3):74-82. doi: 10.11648/j.ajist.20180203.12
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Download@article{10.11648/j.ajist.20180203.12,
author = {Mengdi Jin and Jiwei Liu},
title = {Further Promotion of Quadratic Time-Varying Parameters Discrete Grey Model},
journal = {American Journal of Information Science and Technology},
volume = {2},
number = {3},
pages = {74-82},
doi = {10.11648/j.ajist.20180203.12},
url = {https://doi.org/10.11648/j.ajist.20180203.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajist.20180203.12},
abstract = {Based on the reason that the traditional buffer operator cannot adjust the action intensity, this paper proposes a positive real order weakening buffer operator, which solves the disadvantage that the original operator cannot be fine-tuned, and is more suitable for real life systems. By defining positive real order weakening buffer operator and according to the combination number and the nature of gamma function, the two are connected, and the positive real order weakening buffer sequence is transformed by gamma function. Next a quadratic time-varying linear parameter grey discrete prediction model (QTDGM) is established by using the constructed positive real order weakening buffer operator. The iterative optimization method of simulation base value is given, and the optimization model is established and the solution algorithm is proposed. Finally, the steps of modeling and forecasting by using QDGM model are described. In the case of science popularization fund forecast and raw coal output forecast, QTDGM model shows superior prediction effect. The relative error of the model is 0.34% ~ 7% in the three cases, which is much lower than that of the model using integer order weakening buffer operator and also lower than that of the linear time-varying parameter grey discrete model. QTDGM is more suitable for complex sample systems.},
year = {2018}
}
TY - JOUR T1 - Further Promotion of Quadratic Time-Varying Parameters Discrete Grey Model AU - Mengdi Jin AU - Jiwei Liu Y1 - 2018/12/19 PY - 2018 N1 - https://doi.org/10.11648/j.ajist.20180203.12 DO - 10.11648/j.ajist.20180203.12 T2 - American Journal of Information Science and Technology JF - American Journal of Information Science and Technology JO - American Journal of Information Science and Technology SP - 74 EP - 82 PB - Science Publishing Group SN - 2640-0588 UR - https://doi.org/10.11648/j.ajist.20180203.12 AB - Based on the reason that the traditional buffer operator cannot adjust the action intensity, this paper proposes a positive real order weakening buffer operator, which solves the disadvantage that the original operator cannot be fine-tuned, and is more suitable for real life systems. By defining positive real order weakening buffer operator and according to the combination number and the nature of gamma function, the two are connected, and the positive real order weakening buffer sequence is transformed by gamma function. Next a quadratic time-varying linear parameter grey discrete prediction model (QTDGM) is established by using the constructed positive real order weakening buffer operator. The iterative optimization method of simulation base value is given, and the optimization model is established and the solution algorithm is proposed. Finally, the steps of modeling and forecasting by using QDGM model are described. In the case of science popularization fund forecast and raw coal output forecast, QTDGM model shows superior prediction effect. The relative error of the model is 0.34% ~ 7% in the three cases, which is much lower than that of the model using integer order weakening buffer operator and also lower than that of the linear time-varying parameter grey discrete model. QTDGM is more suitable for complex sample systems. VL - 2 IS - 3 ER -
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