Fully fuzzy polynomial regression with fuzzy neural networks

被引：27
|
作者：
Otadi, Mahmood [1 ]
机构：
[1] Islamic Azad Univ, Dept Math, Firoozkooh Branch, Firoozkooh, Iran
来源：
NEUROCOMPUTING | 2014年 / 142卷
关键词：
Fuzzy regression; Fuzzy neural networks; Learning algorithm; POSSIBILISTIC LINEAR-SYSTEMS; DIFFERENTIAL-EQUATIONS; NUMERICAL-SOLUTION; OUTPUT DATA; MODEL; SIMULATION; WEIGHTS; INPUT;
D O I：
10.1016/j.neucom.2014.03.048
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper a polynomial fuzzy regression model with fuzzy independent variables and fuzzy parameters is discussed. Within this paper the fuzzy neural network model is used to obtain an estimate for the fuzzy parameters in a statistical sense. Based on the extension principle, a simple algorithm from the cost function of the fuzzy neural network is proposed, in order to find the approximate parameters. Finally, we illustrate our approach by some numerical examples. (C) 2014 Elsevier B.V. All rights reserved.
引用
收藏
页码：486 / 493
页数：8
相关论文
共 50 条
  • [31] Fuzzy regression analysis by neural networks with non-symmetric fuzzy number weights
    Ishibuchi, H
    Nii, M
    [J]. ICNN - 1996 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS, VOLS. 1-4, 1996, : 1191 - 1196
  • [32] IG-based genetically optimized fuzzy polynomial neural networks with fuzzy set-based polynomial neurons
    Oh, Sung-Kwun
    Roh, Seok-Beom
    Pedrycz, Witold
    Ahn, Tae-Chon
    [J]. NEUROCOMPUTING, 2007, 70 (16-18) : 2783 - 2798
  • [33] Genetically optimized Hybrid fuzzy Neural Networks based on TSK fuzzy rules and polynomial neurons
    Oh, S
    Park, B
    Kim, H
    [J]. ADVANCES IN NEURAL NETWORKS - ISNN 2005, PT 1, PROCEEDINGS, 2005, 3496 : 404 - 409
  • [34] Fuzzy min–max neural networks: from classification to regression
    R. Tagliaferri
    A. Eleuteri
    M. Meneganti
    F. Barone
    [J]. Soft Computing, 2001, 5 (1) : 69 - 76
  • [35] Evaluation of fuzzy regression models by fuzzy neural network
    Mosleh, M.
    Otadi, M.
    Abbasbandy, S.
    [J]. JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, 2010, 234 (03) : 825 - 834
  • [36] A Design of Genetically Oriented Fuzzy Relation Neural Networks (FrNNs) Based on the Fuzzy Polynomial Inference Scheme
    Park, Byoung-Jun
    Pedrycz, Witold
    Oh, Sung-Kwun
    [J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2009, 17 (06) : 1310 - 1323
  • [37] Genetically optimized hybrid fuzzy neural networks based on simplified fuzzy inference rules and polynomial neurons
    Oh, SK
    Park, LJ
    Pedrycz, W
    Ahn, TC
    [J]. COMPUTATIONAL SCIENCE - ICCS 2005, PT 1, PROCEEDINGS, 2005, 3514 : 798 - 803
  • [38] Fuzzy set-oriented neural networks based on fuzzy polynomial inference and dynamic genetic optimization
    Byoung-Jun Park
    Wook-Dong Kim
    Sung-Kwun Oh
    Witold Pedrycz
    [J]. Knowledge and Information Systems, 2014, 39 : 207 - 240
  • [39] Fuzzy set-oriented neural networks based on fuzzy polynomial inference and dynamic genetic optimization
    Park, Byoung-Jun
    Kim, Wook-Dong
    Oh, Sung-Kwun
    Pedrycz, Witold
    [J]. KNOWLEDGE AND INFORMATION SYSTEMS, 2014, 39 (01) : 207 - 240
  • [40] IG-based genetically optimized Fuzzy Polynomial Neural Networks
    Oh, SK
    Roh, SB
    Pedrycz, W
    Lee, JB
    [J]. COMPUTATIONAL INTELLIGENCE AND BIOINSPIRED SYSTEMS, PROCEEDINGS, 2005, 3512 : 416 - 423
12345