Learning Concurrently Granularity, Membership Function Parameters and Rules of Mamdani Fuzzy Rule-based Systems

被引：0
|
作者：
Antonelli, Michela [1 ]
Ducange, Pietro [1 ]
Lazzerini, Beatrice [1 ]
Marcelloni, Francesco [1 ]
机构：
[1] Univ Pisa, Dipartimento Ingn Informaz Elettron, I-56122 Pisa, Italy
来源：
PROCEEDINGS OF THE JOINT 2009 INTERNATIONAL FUZZY SYSTEMS ASSOCIATION WORLD CONGRESS AND 2009 EUROPEAN SOCIETY OF FUZZY LOGIC AND TECHNOLOGY CONFERENCE | 2009年
关键词：
Accuracy-Interpretability Trade-off; Granularity Learning; Mamdani Fuzzy-Rule-Based Systems; Multi-objective Evolutionary Algorithms; Piecewise Linear Transformation; MULTIOBJECTIVE EVOLUTIONARY APPROACH; IDENTIFICATION; ADAPTATION;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper we tackle the issue of generating Mamdani fuzzy rule-based systems with optimal trade-offs between complexity and accuracy by using a multi-objective genetic algorithm, which concurrently learns rule base, granularity of the input and output partitions and membership function parameters. To this aim, we exploit a chromosome composed of three parts, which codify, respectively, the rule base, and, for each variable, the number of fuzzy sets and the parameters of a piecewise linear transformation of the membership functions. We show the encouraging results obtained on a real world regression problem.
引用
收藏
页码：1033 / 1038
页数：6
相关论文
共 50 条
  • [1] Multi-objective evolutionary learning of granularity, membership function parameters and rules of Mamdani fuzzy systems
    Antonelli M.
    Ducange P.
    Lazzerini B.
    Marcelloni F.
    Evolutionary Intelligence, 2009, 2 (1-2) : 21 - 37
  • [2] Learning concurrently data and rule bases of Mamdani fuzzy rule-based systems by exploiting a novel interpretability index
    Antonelli, Michela
    Ducange, Pietro
    Lazzerini, Beatrice
    Marcelloni, Francesco
    SOFT COMPUTING, 2011, 15 (10) : 1981 - 1998
  • [3] Learning concurrently data and rule bases of Mamdani fuzzy rule-based systems by exploiting a novel interpretability index
    Michela Antonelli
    Pietro Ducange
    Beatrice Lazzerini
    Francesco Marcelloni
    Soft Computing, 2011, 15 : 1981 - 1998
  • [4] Mamdani fuzzy rule-based models for psychological research
    Pandey, Deepak Chandra
    Kushwaha, Govind Singh
    Kumar, Sanjay
    SN APPLIED SCIENCES, 2020, 2 (05):
  • [5] Mamdani fuzzy rule-based models for psychological research
    Deepak Chandra Pandey
    Govind Singh Kushwaha
    Sanjay Kumar
    SN Applied Sciences, 2020, 2
  • [6] TS fuzzy rule-based systems with polynomial membership functions
    Kluska, Jacek
    ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING - ICAISC 2008, PROCEEDINGS, 2008, 5097 : 253 - 264
  • [7] Improving the OVO performance in fuzzy rule-based classification systems by the genetic learning of the granularity level
    Villar, Pedro
    Fernandez, Alberto
    Montes, Rosana
    Maria Sanchez, Ana
    Herrera, Francisco
    2015 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE 2015), 2015,
  • [8] A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems
    Cordon, Oscar
    INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 2011, 52 (06) : 894 - 913
  • [9] Orthogonal Fuzzy Rule-Based Systems: Selection of Optimum Rules
    A. Lotfi
    M. Howarth
    J. B. Hull
    Neural Computing & Applications, 2000, 9 : 4 - 11
  • [10] Exploiting a New Interpretability Index in the Multi-Objective Evolutionary Learning of Mamdani Fuzzy Rule-based Systems
    Antonelli, Michela
    Ducange, Pietro
    Lazzerini, Beatrice
    Marcelloni, Francesco
    2009 9TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, 2009, : 115 - 120
12345