Evolutionally optimized fuzzy neural networks based on evolutionary fuzzy granulation

被引：0
|
作者：
Oh, SK
Park, BJ
Pedrycz, W
Kim, HK
机构：
[1] Univ Suwon, Dept Elect Engn, Hwaseong 445743, Gyeonggi, South Korea
[2] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6G 2G6, Canada
[3] Polish Acad Sci, Syst Res Inst, Warsaw, Poland
来源：
COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2005, VOL 4, PROCEEDINGS | 2005年 / 3483卷
关键词：
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
In this paper, new architectures and comprehensive design methodologies of Genetic Algorithms (GAs) based Evolutionally optimized Fuzzy Neural Networks (EoFNN) are introduced and the dynamic search-based GAs is introduced to lead to rapidly optimal convergence over a limited region or a boundary condition. The proposed EoFNN is based on the Fuzzy Neural Networks (FNN) with the extended structure of fuzzy rules being formed within the networks. In the consequence part of the fuzzy rules, three different forms of the regression polynomials such as constant, linear and modified quadratic are taken into consideration. The structure and parameters of the EoFNN are optimized by the dynamic search-based GAs.
引用
收藏
页码：887 / 895
页数：9
相关论文
共 50 条
  • [1] Evolutionally optimized fuzzy neural networks based on fuzzy relation rules and evolutionary data granulation
    Oh, SK
    Kim, HK
    Jang, SW
    Kim, YK
    [J]. AI 2005: ADVANCES IN ARTIFICIAL INTELLIGENCE, 2005, 3809 : 1075 - 1078
  • [2] Design of evolutionally optimized rule-based fuzzy neural networks based on fuzzy relation and evolutionary optimization
    Park, BJ
    Oh, SK
    Pedrycz, W
    Kim, HK
    [J]. COMPUTATIONAL SCIENCE - ICCS 2005, PT 3, 2005, 3516 : 1100 - 1103
  • [3] Fuzzy granulation-based cascade fuzzy neural networks optimized by GA-RSL
    Han, Chang-Wook
    Park, Jung-Il
    [J]. ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 2006, 3955 : 77 - 86
  • [4] Design of fuzzy neural networks based on genetic fuzzy granulation and regression polynomial fuzzy inference
    Oh, Sung-Kwun
    Park, Byoung-Jun
    Pedrycz, Witold
    [J]. ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 1, 2006, 3971 : 786 - 791
  • [5] Genetically optimized self-organizing fuzzy polynomial neural networks based on information granulation
    Park, H
    Park, D
    Oh, S
    [J]. ADVANCES IN NEURAL NETWORKS - ISNN 2005, PT 1, PROCEEDINGS, 2005, 3496 : 410 - 415
  • [6] Self-organizing neurofuzzy networks based on evolutionary fuzzy granulation
    Oh, SK
    Pedrycz, W
    Park, BJ
    [J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS, 2003, 33 (02): : 271 - 277
  • [7] Genetically dynamic optimized self-organizing fuzzy polynomial neural networks with information granulation based FPNs
    Park, HS
    Oh, SK
    Pedrycz, W
    Kim, HK
    [J]. MODELING DECISIONS FOR ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 2005, 3558 : 346 - 353
  • [8] Fuzzy evolutionary probabilistic neural networks
    Georgiou, V. L.
    Alevizos, Ph. D.
    Vrahatis, M. N.
    [J]. ARTIFICIAL NEURAL NETWORKS IN PATTERN RECOGNITION, PROCEEDINGS, 2008, 5064 : 113 - 124
  • [9] Genetically optimized Hybrid Fuzzy Neural Networks based on linear fuzzy inference rules
    Oh, SK
    Park, BJ
    Kim, HK
    [J]. INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, 2005, 3 (02) : 183 - 194
  • [10] Evolutionary algorithm-based learning of fuzzy neural networks. Part 2: Recurrent fuzzy neural networks
    Aliev, R. A.
    Guirimov, B. G.
    Fazlollahi, Bijan
    Aliev, R. R.
    [J]. FUZZY SETS AND SYSTEMS, 2009, 160 (17) : 2553 - 2566
12345