东北大学学报:自然科学版 ›› 2016, Vol. 37 ›› Issue (10): 1379-1383.DOI: 10.12068/j.issn.1005-3026.2016.10.003

• 信息与控制 • 上一篇    下一篇

基于QPSO算法优化的区间二型模糊逻辑系统预测

陈阳, 王大志, 宁武   

  1. (东北大学 信息科学与工程学院, 辽宁 沈阳110819)
  • 收稿日期:2015-06-29 修回日期:2015-06-29 出版日期:2016-10-15 发布日期:2016-10-14
  • 通讯作者: 陈阳
  • 作者简介:陈阳(1981-),男,辽宁大连人,东北大学博士研究生; 王大志(1963-),男,辽宁锦州人,东北大学教授,博士生导师.
  • 基金资助:
    国家自然科学基金资助项目(61374113); 中央高校基本科研业务费专项资金资助项目(N150403005); 辽宁省高校基本科研业务费资助项目(JL201615410).

Forecasting by Interval Type-2 Fuzzy Logic System Optimized with QPSO Algorithm

CHEN Yang, WANG Da-zhi, NING Wu   

  1. School of Information Sciences & Engineering, Northeastern University, Shenyang 110819, China.
  • Received:2015-06-29 Revised:2015-06-29 Online:2016-10-15 Published:2016-10-14
  • Contact: CHEN Yang
  • About author:-
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摘要: 设计了一类区间二型模糊逻辑系统,研究基于历史数据的预测问题.在区间二型模糊逻辑系统设计中,前件、后件、输入测量区间二型模糊的主隶属函数均选择成具有不确定标准偏差的高斯型二型隶属函数.量子粒子群优化 (QPSO) 算法用来调整所设计的区间二型模糊逻辑系统参数.部分欧洲智能技术网络 (EUNITE) 的负荷竞赛数据和美国田纳西州 (WTI) 原油价格数据用来测试所提出的模糊逻辑系统预测方法.定义综合评价误差和作为模糊逻辑系统的预测性能指标.仿真研究表明,所提出的区间二型模糊逻辑系统预测方法在收敛性和稳定性上均优于相应的一型模糊逻辑系统.

关键词: 区间二型模糊逻辑系统, 区间二型模糊集, 量子粒子群优化算法, 仿真, 收敛性

Abstract: A kind of interval type-2 fuzzy logic system was designed to investigate forecasting problems based on the historical data. In the process of designing the interval type-2 fuzzy logic system, the antecedent, consequent and input measurement primary membership functions of interval type-2 fuzzy sets were all Gaussian type-2 membership functions with uncertain standard deviation. The quantum particle swarm optimization algorithm was used to tune the parameters of the designed interval type-2 fuzzy logic system. Part of the load competition data of European network on intelligent technologies and the price data of West Texas Intermediate crude oil were used to test the proposed fuzzy logic system forecasting method. Comprehensive evaluation error sum was defined as the forecasting performance index of fuzzy logic system. Simulation studies showed that the proposed interval type-2 fuzzy logic system forecasting methods outperform their corresponding type-1 fuzzy logic system on convergence and stability.

Key words: interval type-2 fuzzy logic system, interval type-2 fuzzy set, quantum particle swarm optimization algorithm, simulation, convergence

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