[1]王亚男,雷英杰,雷阳,等.高阶多元直觉模糊时间序列预测模型[J].东南大学学报(自然科学版),2016,46(3):505-512.[doi:10.3969/j.issn.1001-0505.2016.03.009]
 Wang Yanan,Lei Yingjie,Lei Yang,et al.High-order multi-variable intuitionistic fuzzy time series forecasting model[J].Journal of Southeast University (Natural Science Edition),2016,46(3):505-512.[doi:10.3969/j.issn.1001-0505.2016.03.009]
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高阶多元直觉模糊时间序列预测模型()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
46
期数:
2016年第3期
页码:
505-512
栏目:
计算机科学与工程
出版日期:
2016-05-20

文章信息/Info

Title:
High-order multi-variable intuitionistic fuzzy time series forecasting model
作者:
王亚男1雷英杰1雷阳2范晓诗1
1空军工程大学防空反导学院, 西安 710051; 2武警工程大学电子技术系, 西安 710086
Author(s):
Wang Yanan1 Lei Yingjie1 Lei Yang2 Fan Xiaoshi1
1Air and Missile Defense College, Air Force Engineering University, Xi’an 710051, China
2Department of Electronics Technology, Engineering University of Armed Police Force, Xi’an 710086, China
关键词:
高阶 多元 直觉模糊时间序列 直觉模糊多维取式推理
Keywords:
high-order multi-variables intuitionistic fuzzy time series multidimensional intuitionistic fuzzy modus ponens reasoning
分类号:
TP393.08
DOI:
10.3969/j.issn.1001-0505.2016.03.009
摘要:
为了突破模糊集理论的限制,更客观地描述不确定性数据,提出一种高阶多元直觉模糊时间序列预测模型.采用模糊聚类算法划分论域,并采用更具客观性的方法建立直觉模糊集的隶属度和非隶属度函数.依据直觉模糊多维取式推理的原理建立基于相似度量的启发式推理规则,作为高阶多元模型的预测规则,并且建立相应的解模糊方法.利用北京市日均气温数据集进行对比实验,结果表明,该模型的预测均方误差(0.86)和平均预测误差(2.57%)较现有方法均明显降低,预测结果优于模糊时间序列预测模型和普通直觉模糊时间序列预测模型.
Abstract:
In order to break the limitation of fuzzy set theory and objectively describe the uncertain data, a high-order multi-variable intuitionistic fuzzy time series forecasting model is proposed. A fuzzy clustering algorithm is adopted to partition the universe of discourse, and a more objective method is used to establish the membership and non-membership functions of intuitionistic fuzzy sets. According to the principle of multidimensional intuitionistic fuzzy modus ponens reasoning, a heuristic similarity-based reasoning technique is proposed as the forecasting rule of the high-order multi-variable forecasting model, and a corresponding defuzzification method is presented. Contrast experiments on the daily mean temperature of Beijing were carried out. Experimental results show that the root mean square error(0.86)and the average forecasting error(2.57%)of the proposed model both obviously decreased. Therefore, the forecasting performance of the model is better than that of the fuzzy time series forecasting models and the normal intuitionistic fuzzy time series forecasting models.

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备注/Memo

备注/Memo:
收稿日期: 2015-09-23.
作者简介: 王亚男(1988—),女,博士生;雷英杰(联系人),男,博士,教授,博士生导师,leiyjie@163.com.
基金项目: 国家自然科学基金青年科学基金资助项目(61309022).
引用本文: 王亚男,雷英杰,雷阳,等.高阶多元直觉模糊时间序列预测模型[J].东南大学学报(自然科学版),2016,46(3):505-512. DOI:10.3969/j.issn.1001-0505.2016.03.009.
更新日期/Last Update: 2016-05-20