Please wait a minute...
浙江大学学报(理学版)  2024, Vol. 51 Issue (2): 196-204    DOI: 10.3785/j.issn.1008-9497.2024.02.008
数学与计算机科学     
基于Conformable分数阶导数的灰色Bernoulli模型
骆世广1(),曾亮2()
1.广东金融学院 金融数学与统计学院,广东 广州 510521
2.广东理工学院 基础课教学研究部,广东 肇庆 526100
Grey Bernoulli model based on Conformable fractional order derivatives
Shiguang LUO1(),Liang ZENG2()
1.School of Financial Mathematics and Statistics,Guangdong University of Fiance,Guangzhou 510521,China
2.Department of Basic Courses,Guangdong Technology College,Zhaoqing 526100,Guangdong Province,China
 全文: PDF(2981 KB)   HTML( 2 )
摘要:

为增强灰色Bernoulli模型对各种实际数据序列的适应性,借助分数阶微积分在描述复杂系统中的优势,提出了一种基于Conformable分数阶导数的灰色Bernoulli模型。研究发现,可通过改变结构参数将模型转换为不同的经典灰色预测模型,体现了其统一性。此外,采用粒子群优化算法求解规划模型,获取了模型的最优超参数。最后,用所提模型和5个竞争模型对3个真实案例进行了预测建模,结果表明,所提模型的2项评估指标均优于5个竞争模型,验证了所提模型的有效性和可行性。

关键词: 灰色系统Conformable分数阶导数灰色Bernoulli模型粒子群优化算法    
Abstract:

To enhance the adaptability of the grey Bernoulli model to various real data series, a grey Bernoulli model based on Conformable fractional order derivatives is proposed by taking advantage of the fractional order calculus in describing complex systems. It is found that the proposed model can be converted to some classical grey prediction models by replacing its structural parameters, which reflects its uniformity. Moreover, the particle swarm optimization algorithm is used to solve the planning model to obtain the optimal hyperparameters of the proposed model. The experimental results show that the two evaluation indicators of the proposed model are superior to the five competing algorithms, which confirms the validity and feasibility of the new model.

Key words: grey system    Conformable fractional derivative    grey Bernoulli model    particle swarm optimization algorithm
收稿日期: 2022-08-10 出版日期: 2024-03-08
CLC:  N 941.5  
基金资助: 广东省教育科学“十三五”规划2020年度研究项目(2020JKDY040)
通讯作者: 曾亮     E-mail: sgluomaths@gduf.edu.cn;zengliang19820809@126.com
作者简介: 骆世广(1981—),ORCID:https://orcid.org/0009-0003-3345-4332,男,硕士,副教授,主要从事金融数据挖掘、灰色系统理论研究,E-mail:sgluomaths@gduf.edu.cn.
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
骆世广
曾亮

引用本文:

骆世广,曾亮. 基于Conformable分数阶导数的灰色Bernoulli模型[J]. 浙江大学学报(理学版), 2024, 51(2): 196-204.

Shiguang LUO,Liang ZENG. Grey Bernoulli model based on Conformable fractional order derivatives. Journal of Zhejiang University (Science Edition), 2024, 51(2): 196-204.

链接本文:

https://www.zjujournals.com/sci/CN/10.3785/j.issn.1008-9497.2024.02.008        https://www.zjujournals.com/sci/CN/Y2024/V51/I2/196

图1  不同参数值下的还原式曲线
序号原始数据GM(1,1)GVMNGBM(1,1)DFNGBM(1,1,αCFTDNGBMCFGBM(μ,1)
143.1943.1943.1943.1943.1943.1943.19
258.7364.2529.3558.7358.7358.7358.73
370.8771.4646.0371.9071.9170.8872.76
483.7179.4867.4182.4183.1883.6883.71
592.9188.4089.4891.3092.5892.9692.61
699.7398.33104.4099.0999.9899.70100.02
7105.08109.37104.98106.05105.12105.08106.25
8109.73121.6490.90112.36107.60110.10111.54
9112.19135.3069.07118.15106.86115.65116.04
10113.45150.4947.44123.50102.07122.59119.88
训练集MAPE3.6616.150.900.390.020.63
RMSE3.5715.940.980.470.030.85
测试集MAPE21.3737.935.525.573.833.58
RMSE26.1346.806.917.365.654.45
表1  6个预测模型对某工程施工地基沉降量的预测结果
年份原始数据GM(1,1)GVMNGBM(1,1)DFNGBM(1,1,αCFTDNGBMCFGBM(μ,1)
200814 535.4014 535.4014 535.4014 535.4014 535.4014 535.4014 535.40
200917 541.9217 617.8010 286.0017 541.6517 541.0817 541.9217 541.64
201019 980.3920 455.0716 037.5620 473.2019 985.5919 980.3920 878.03
201124 345.9123 749.2622 821.9723 793.3024 354.5324 345.9024 345.81
201228 119.0027 573.9728 585.0527 596.6128 043.2628 119.0228 086.19
201331 668.9532 014.6330 587.9331 970.7431 794.6731 668.9432 182.36
201435 312.4037 170.4427 680.6537 010.2435 524.3035 017.0736 701.95
201540 974.6443 156.5621 499.7842 821.5839 264.5338 201.5341 709.18
201646 344.8850 106.7214 800.9049 526.3743 021.4941 247.3447 269.85
训练集MAPE1.3812.071.260.120.001.04
RMSE408.993 461.75389.9260.060.01422.38
测试集MAPE6.2345.735.393.986.202.57
RMSE2 730.3621 852.052 339.212 161.353 354.711 052.93
表2  6个预测模型对中国卫生总费用的预测结果
年份原始数据GM(1,1)GVMNGBM(1,1)DFNGBM(1,1, αCFTDNGBMCFGBM(μ,1)
20034.004.004.004.004.004.004.00
20045.206.451.694.595.225.194.31
20056.107.512.376.386.106.106.10
20067.808.753.328.248.128.098.08
200710.9010.194.5910.1810.2310.3110.20
200812.8011.866.2812.2012.3912.4812.41
200913.3013.818.4214.3214.6014.6314.69
201017.0016.0811.0316.5316.8416.7817.00
201119.7018.7314.0118.8419.1318.9819.31
201221.3021.8117.0821.2721.4421.2921.61
201323.8025.3919.8523.8023.8023.8023.87
201425.1029.5721.8126.4526.1826.5826.07
201526.2034.4322.5429.2328.6029.7428.21
201627.5040.0921.8632.1431.0533.3930.26
201730.3046.6919.9335.1833.5337.6432.23
201833.6054.3617.1838.3636.0542.6434.11
训练集MAPE8.7739.274.382.552.444.03
RMSE0.994.800.570.500.510.58
测试集MAPE42.1826.1412.828.8618.395.89
RMSE13.759.313.982.686.081.82
表3  6个预测模型对中国人均天然气生活消费量的预测结果
图2  各模型在3个案例中的MAPE和RMSE
1 邓聚龙. 灰理论基础[M]. 武汉: 华中科技大学出版社, 2002: 300-309.
DENG J L. Fundamentals of Grey Theory[M]. Wuhan: Huazhong University of Science and Technology Press, 2002: 300-309.
2 CHEN C I. Application of the novel nonlinear grey Bernoulli model for forecasting unemployment rate[J]. Chaos, Solitons & Fractals, 2008, 37(1): 278-287. DOI:10.1016/j.chaos.2006.08.024
doi: 10.1016/j.chaos.2006.08.024
3 王正新, 党耀国, 刘思峰. 非等间距GM(1,1)幂模型及其工程应用[J]. 中国工程科学, 2012, 14(7): 98-102. doi:10.3969/j.issn.1009-1742.2012.07.015
WANG Z X, DANG Y G, LIU S F. Non-equidistant GM(1,1) power model and its application in engineering[J]. Strategic Study of Chinese Academy of Engineering, 2012, 14(7): 98-102. doi:10.3969/j.issn.1009-1742.2012.07.015
doi: 10.3969/j.issn.1009-1742.2012.07.015
4 王正新. 时变参数GM(1,1)幂模型及其应用[J]. 控制与决策, 2014, 29(10): 1828-1832. DOI:10. 13195/j.kzyjc.2013.1006
WANG Z X. GM(1,1) power model with time-varying parameters and its application[J]. Control and Decision, 2014, 29(10): 1828-1832. DOI:10. 13195/j.kzyjc.2013.1006
doi: 10. 13195/j.kzyjc.2013.1006
5 MA X, LIU Z B, WANG Y. Application of a novel nonlinear multivariate grey Bernoulli model to predict the tourist income of China[J]. Journal of Computational and Applied Mathematics, 2019, 347: 84-94. DOI:10.1016/j.cam.2018.07.044
doi: 10.1016/j.cam.2018.07.044
6 WU W Q, MA X, ZENG B, et al. Forecasting short-term renewable energy consumption of China using a novel fractional nonlinear grey Bernoulli model[J]. Renewable Energy, 2019, 140: 70-87. DOI:10.1016/j.renene.2019.03.006
doi: 10.1016/j.renene.2019.03.006
7 WU W Q, MA X, ZENG B, et al. A novel grey Bernoulli model for short-term natural gas consumption forecasting[J]. Applied Mathematical Modelling, 2020, 84: 393-404. DOI:10.1016/j.apm.2020.04.006
doi: 10.1016/j.apm.2020.04.006
8 ZHENG C L, WU W Z, XIE W L, et al. A MFO-based conformable fractional nonhomogeneous grey Bernoulli model for natural gas production and consumption forecasting[J]. Applied Soft Computing, 2021, 99: 106891. DOI:10.1016/j.asoc.2020.106891
doi: 10.1016/j.asoc.2020.106891
9 WANG R, LIU H, YANG Q W, et al. A new conformable fractional-order time-delay grey Bernoulli model with the arithmetic optimization algorithm and its application in rural regional economy[J]. Journal of Mathematics(Open Access), 2023. DOI:10.1155/2023/2017167
doi: 10.1155/2023/2017167
10 LAO T F, SUN Y R. Predicting the production and consumption of natural gas in China by using a new grey forecasting method[J]. Mathematics and Computers in Simulation, 2022, 202: 295-315. DOI:10.1016/j.matcom.2022.05.023
doi: 10.1016/j.matcom.2022.05.023
11 KANG Y X, MAO S H, ZHANG Y H, et al. Fractional derivative multivariable grey model for nonstationary sequence and its application[J]. Journal of Systems Engineering and Electronics, 2020, 31(5): 1009-1018. DOI:10.23919/jsee.2020.000075
doi: 10.23919/jsee.2020.000075
12 吴利丰, 刘思峰, 姚立根. 含Caputo 型分数阶导数的灰色预测模型[J]. 系统工程理论与实践, 2015, 35(5): 1311-1316. DOI:10.12011/1000-6788(2015)5-1311
WU L F, LIU S F, YAO L G. Grey model with Caputo fractional order derivative[J]. Systems Engineering-Theory & Practice, 2015, 35(5): 1311-1316. DOI:10.12011/1000-6788(2015)5-1311
doi: 10.12011/1000-6788(2015)5-1311
13 WU W Q, MA X, WANG Y, et al. Research on a novel fractional GM(αn) model and its applications[J]. Grey Systems (Theory and Application), 2019, 9(3): 356-373. DOI:10.1108/gs-11-2018-0052
doi: 10.1108/gs-11-2018-0052
14 YANG Y, XUE D Y. Continuous fractional-order grey model and electricity prediction research based on the observation error feedback[J]. Energy, 2016, 115: 722-733. DOI:10.1016/j.energy.2016.08.097
doi: 10.1016/j.energy.2016.08.097
15 MAO S H, KANG Y X, ZHANG Y H, et al. Fractional grey model based on non-singular exponential kernel and its application in the prediction of electronic waste precious metal content[J]. ISA Transactions, 2020, 107: 12-26. DOI:10.1016/j.isatra.2020.07.023
doi: 10.1016/j.isatra.2020.07.023
16 KHALIL R, HORANI M AL, YOUSEF A, et al. A new definition of fractional derivative[J]. Journal of Computational and Applied Mathematics, 2014, 264: 65-70. DOI:10.1016/j.cam.2014.01.002
doi: 10.1016/j.cam.2014.01.002
17 ABDELJAWAD T. On conformable fractional calculus[J]. Journal of Computational and Applied Mathematics, 2015, 279: 57-66. DOI:10.1016/j.cam.2014.10.016
doi: 10.1016/j.cam.2014.10.016
18 MA X, WU W Q, ZENG B, et al. The conformable fractional grey system model[J]. ISA Transactions, 2020, 96: 255-271. DOI:10.1016/j.isatra.2019. 07.009
doi: 10.1016/j.isatra.2019. 07.009
19 XIE W L, LIU C X, WU W Z, et al. Continuous grey model with conformable fractional derivative[J]. Chaos, Solitons & Fractals, 2020, 139: 110285. DOI:10.1016/j.chaos.2020.110285
doi: 10.1016/j.chaos.2020.110285
20 WU W Q, MA X, ZENG B, et al. A novel multivariate grey system model with conformable fractional derivative and its applications[J]. Computers & Industrial Engineering, 2022, 164: 107888. DOI:10.1016/j.cie.2021.107888
doi: 10.1016/j.cie.2021.107888
21 WANG S, JIANG W, SHENG J L, et al. Ulam's stability for some linear conformable fractional differential equations[J]. Advances in Difference Equations, 2020, 2020(1): 1-18. DOI:10.1186/s13662-020-02672-3
doi: 10.1186/s13662-020-02672-3
22 WU L F, XU Z C. Analyzing the air quality of Beijing, Tianjin, and Shijiazhuang using grey Verhulst model[J]. Air Quality Atmosphere & Health, 2019, 12(8): 1-8. DOI:10.1007/s11869-019-00746-0
doi: 10.1007/s11869-019-00746-0
23 WU W Q, MA X, ZENG B, et al. Forecasting short-term renewable energy consumption of China using a novel fractional nonlinear grey Bernoulli model[J]. Renewable Energy, 2019, 140: 70-87. DOI:10.1016/j.renene.2019.03.006
doi: 10.1016/j.renene.2019.03.006
24 YU L, MA X, WU W Q, et al. A novel elastic net-based NGBMC(1,n) model with multi-objective optimization for nonlinear time series forecasting[J]. Communications in Nonlinear Science and Numerical Simulation, 2021, 96: 105696. DOI:10. 1016/j.cnsns.2021.105696
doi: 10. 1016/j.cnsns.2021.105696
25 史峰. MATLAB智能算法30个案例分析[M]. 北京: 北京航空航天大学出版社, 2011.
SHI F. Analysis of 30 Cases of MATLAB Intelligent Algorithms[M]. Beijing: Beihang University Press, 2011.
26 ZHANG P, MA X, SHE K. A novel power-driven fractional accumulated grey model and its application in forecasting wind energy consumption of China[J]. PLoS One, 2019, 14(12): e0225362. DOI:10.1371/journal.pone.0225362
doi: 10.1371/journal.pone.0225362
[1] 曾亮. 基于振荡序列的灰色GM(1,1|sin)幂模型及其应用[J]. 浙江大学学报(理学版), 2019, 46(6): 697-704.
[2] 翟国庆1, 张邦俊1, 姚玉鑫2. 利用 GM( 1, 1)模型的数值解法 计算铁路噪声与振动的传播 [J]. 浙江大学学报(理学版), 2000, 27(2): 193-195.