Simplification of Neuro-Fuzzy Models

被引:0
|
作者
Siminski, Krzysztof [1 ]
机构
[1] Silesian Tech Univ, Inst Informat, PL-44100 Gliwice, Poland
来源
MAN-MACHINE INTERACTIONS | 2009年 / 59卷
关键词
neuro-fuzzy system; hierarchical partition; simplification; INFERENCE SYSTEM; IDENTIFICATION;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The neuro-fuzzy system presented in the paper is a system with parameterized consequences implementing hierarchical partition of the input domain. The regions are described with attributes values. In this system not all attribute values must be used to constitute the region. The attributes of minor importance may be ignored. The results of experiments show that the simplified model have less parameters and can achieve better generalisation ability.
引用
收藏
页码:265 / 272
页数:8
相关论文
共 50 条
  • [1] Neuro-fuzzy identification models
    Matko, D
    Karba, R
    Zupancic, B
    [J]. PROCEEDINGS OF IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY 2000, VOLS 1 AND 2, 2000, : 650 - 655
  • [2] Neuro-fuzzy based nonlinear models
    Nitu, C.
    Dobrescu, A.
    [J]. DEVICE APPLICATIONS OF NONLINEAR DYNAMICS, 2006, : 237 - 244
  • [3] Hierarchical neuro-fuzzy quadtree models
    de Souza, FJ
    Vellasco, MMR
    Pacheco, MAC
    [J]. FUZZY SETS AND SYSTEMS, 2002, 130 (02) : 189 - 205
  • [4] Neuro-Fuzzy Systems: Learning models
    de Carvalho, L. F.
    Monteiro, L. L.
    Nassar, S. M.
    de Azevedo, F. M.
    de Carvalho, H. J. T.
    [J]. International Conference on Computational Intelligence for Modelling, Control & Automation Jointly with International Conference on Intelligent Agents, Web Technologies & Internet Commerce, Vol 2, Proceedings, 2006, : 1122 - 1125
  • [5] Conjunction and Disjunction Operators in Neuro-Fuzzy Risk Calculation Model Simplification
    Toth-Laufer, E.
    Takacs, M.
    Rudas, I. J.
    [J]. 13TH IEEE INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND INFORMATICS (CINTI 2012), 2012, : 195 - 200
  • [6] Design and simplification of adaptive Neuro-Fuzzy inference controllers for power plants
    Alturki, FA
    Abdennour, A
    [J]. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 1999, 21 (07) : 465 - 474
  • [7] Models of neuro-fuzzy agents in intelligent environments
    Shvetcov, Anatoliy
    [J]. XII INTERNATIONAL SYMPOSIUM INTELLIGENT SYSTEMS 2016, (INTELS 2016), 2017, 103 : 135 - 141
  • [8] Extended neuro-fuzzy models of multilayer perceptrons
    Zhang, D
    Bal, XL
    Cai, KY
    [J]. FUZZY SETS AND SYSTEMS, 2004, 142 (02) : 221 - 242
  • [9] Parameter identification of TSK neuro-fuzzy models
    Banakar, Ahmad
    Azeem, Mohammad Fazle
    [J]. FUZZY SETS AND SYSTEMS, 2011, 179 (01) : 62 - 82
  • [10] ADDITIONAL TRAINING OF NEURO-FUZZY DIAGNOSTIC MODELS
    Oliinyk, A.
    Subbotin, S.
    Leoshchenko, S.
    Ilyashenko, M.
    Myronova, N.
    Mastinovsky, Y.
    [J]. RADIO ELECTRONICS COMPUTER SCIENCE CONTROL, 2018, (03) : 106 - 119