| 基于时滞效应的多变量离散灰色预测模型 |
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| 引用本文： | 丁松,党耀国,徐宁,魏龙,叶璟.基于时滞效应的多变量离散灰色预测模型[J].控制与决策,2017,32(11):1997-2004. |
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| 作者姓名： | 丁松 党耀国 徐宁 魏龙 叶璟 |
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| 作者单位： | 南京航空航天大学经济与管理学院,南京211106,南京航空航天大学经济与管理学院,南京211106,南京审计大学管理科学与工程学院,南京211815,南京航空航天大学经济与管理学院,南京211106,南京航空航天大学经济与管理学院,南京211106 |
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| 基金项目： | 国家自然科学基金项目(71371098)；中央高校基本科研业务费专项基金项目(2017301)；江苏省普通高校学术学位研究生创新计划项目(KYZZ16_0153)；南京航空航天大学博士学位论文创新与创优基金项目(BCXJ16-09)；江苏省高校自然科学研究项目(16KJD120001)；江苏省社科基金重点研究项目(16GLA001). |
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| 摘 要： | 针对多变量灰色模型存在驱动项时滞效应作用机制不明确和模型精度不高的问题,分析传统多变量灰色模型的缺陷,通过引入滞后系数控制驱动项,提出考虑时滞累积效应的多变量离散灰色预测模型,讨论模型参数估计的求解方法;从白化信息充分和匮乏两个角度,利用经验分析法和粒子群算法探索时滞效应控制系数的识别方法,并给出模型建模预测步骤;最后,通过实例分析验证模型的有效性,结果表明该模型能够较好地解决有时滞特征的小样本多变量系统的预测问题.
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| 关 键 词： | 灰色系统 多变量 时滞效应 预测 |
Multi-variable time-delayed discrete grey model |
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| DING Song,DANG Yao-guo,XU Ning,WEI Long and YE Jing.Multi-variable time-delayed discrete grey model[J].Control and Decision,2017,32(11):1997-2004. |
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| Authors: | DING Song DANG Yao-guo XU Ning WEI Long and YE Jing |
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| Affiliation: | College of Economics and Management,Nanjing University of Aeronauticsand Astronautics,Nanjing211106,China,College of Economics and Management,Nanjing University of Aeronauticsand Astronautics,Nanjing211106,China,College of Management Science and Engineering,Nanjing Audit University,Nanjing211815,China,College of Economics and Management,Nanjing University of Aeronauticsand Astronautics,Nanjing211106,China and College of Economics and Management,Nanjing University of Aeronauticsand Astronautics,Nanjing211106,China |
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| Abstract: | For the problems of undefined time-delayed mechanism and low accuracy of multi-variable grey models, the deficiency of traditional multi-variable grey model is analyzed and a new multi-variable discrete grey model is proposed by introducing the time-delay coefficient. Then the methods of calculating the parameters are discussed. According to the sufficient and insufficient information about the time-delay coefficient, empirical analysis and particle swarm optimization algorithms are used to explore the estimation of time-delay coefficient, and the modeling procedure is proposed. Finally, the effectiveness of the proposed model is verified by the numerical simulation and actual examples. Results show that the propoved model can the forecasting problems on the multi-variable time-delayed system with small sample data. |
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