• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 26 Issue 3
Jun.  2013
Turn off MathJax
Article Contents
Journal of Southwest Jiaotong University  > 2013  >  26(3): 481-486.
YAO Lan, XIAO Jian, JIANG Yulian. Interval TypeⅡ Fuzzy Model Simplification Based on Orthogonal Transformation Methods[J]. Journal of Southwest Jiaotong University, 2013, 26(3): 481-486. doi: 10.3969/j.issn.0258-2724.2013.03.014
Citation: YAO Lan, XIAO Jian, JIANG Yulian. Interval TypeⅡ Fuzzy Model Simplification Based on Orthogonal Transformation Methods[J]. Journal of Southwest Jiaotong University, 2013, 26(3): 481-486. doi: 10.3969/j.issn.0258-2724.2013.03.014
shu

Interval TypeⅡ Fuzzy Model Simplification Based on Orthogonal Transformation Methods

doi: 10.3969/j.issn.0258-2724.2013.03.014
  • Received Date: 03 May 2012
  • Publish Date: 25 Jun 2013
    Abstract
  • As the effective singular value is hard to determine in the singular value decomposition-QR (SVD-QR), QR decomposition with column pivoting (pivoted-QR) was proposed to analyze the fuzzy model structure. By applying it to the two firing strength matrices of the fuzzy model, the absolute values of R-diagonal elements were used as a rule ranking index, and specific rule was located according to the position of element with the value of each column of Π equaling one. Finally, a chaos time series was predicted with the SVD-QR and pivoted-QR, and adaptability of important rules selected by both methods were compared with different samples. The simulation results indicate that the two methods are clearly distinct in the selection of a set of important fuzzy rules. The error of pivoted-QR is 0.108 6 in average, much less than that of the QR. The errors of pivoted-QR with different input samples are close, demonstrating that it has better generalization performance.

     

    • FullText(HTML)
  • loading
    • References(15)
  • KARNIK N N, MENDEL J M, LIANG Q. Type-2 fuzzy logic systems[J]. IEEE Transctions on Fuzzy Systems, 1999, 7(6): 643-658.
    ZADEH L A. The concept of a linguistic variable and its application to approximate reasoning[J]. Information Sciences, 1975, 8(3): 199-249.
    LIU C F, YEH C Y, LEE S J. Application of type-2 neuro-fuzzy modeling in stock price prediction[J]. Applied Soft Computing, 2012, 12(4): 1348-1358.
    CHEN C C, VACHTSEVANOS G. Bearing condition prediction considering uncertainty: an interval type-2 fuzzy neural network approach[J]. Robotics and Computer-Integrated Manufacturing, 2012, 28(4): 509-516.
    MENDEL J M, JOHN R I. Type-2 fuzzy sets made simple[J]. IEEE Transctions on Fuzzy Systems, 2002, 10(2): 117-127.
    SETNES M, BABUSKA R. Rule base reduction: some comments on the use of orthogonal transforms[J]. IEEE Transctions on Systems, Man, and Cybernetics, Part C, 2001, 31(2): 199-206.
    YAM Y, BARANYI P, YANG C T. Reduction of fuzzy rule base via singular value decomposition[J]. IEEE Transctions on Fuzzy Systems, 1999, 7(2): 120-132.
    LIANG Qilian, MENDEL J M. Designing interval type-2 fuzzy logic systems using an SVD-QR method: rule reduction[J]. International Journal of Intelligent Systems, 2000, 15(10): 939-957.
    LIANG Qilian, MENDEL J M. Interval type-2 fuzzy logic systems: theory and design[J]. IEEE Transctions on Fuzzy Systems, 2000, 8(5): 535-550.
    JOHN R, COUPLAND S. Type-2 fuzzy logic: a historical view[J].Computational Intelligence Magazine, 2007, 2(1): 57-62.
    GOLUB G H, van LOAN C F. Matrix computations[M]. 3rd ed. Baltimore: Johns Hopkins University Press, 1996: 199-235.
    WU Dongrui, MENDEL J M. Enhanced Karnik-Mendel algorithms[J]. IEEE Transactions on Fuzzy Systems, 2009, 17(4): 923-934.
    MACKEY M C, GLASS L. Oscillation and chaos in physiological control systems[J]. Science, 1977, 197(1): 287-289.
    丁伟,黄景春. 基于多分辨率分析的区间类型2模糊系统[J]. 西南交通大学学报,2011,46(6): 917-921.DING Wei, HUANG Jingchun. Interval type-2 fuzzy logic system based on multi-resolution analysis[J]. Journal of Southwest Jiaotong University, 2011, 46(6): 917-921.
    YAO Lan, JIANG Yulian. Short-term power load forecasting by interval type-2 fuzzy logic system[C]//Proceedings of 2nd International Conference on Information Computing and Applications. Berlin: Springer-Verlag, 2011(2): 575-582.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML
    Article views(901) PDF downloads(439) Cited by()
    Proportional views
    Related

    Copyright © 2009 Journal of Southwest Jiaotong University

    Address: RM449 & 451, Administrative Building, Xipu Campus, Southwest Jiaotong University Western Hi-tech Zone, Chengdu, Sichuan 611756, P. R. China

    Tel: 028-66367562Fax: 028-66366552Email: xbz@home.swjtu.edu.cn

    Supported by: Beijing Renhe Information Technology Co. Ltd  support: info@rhhz.net

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return