Výsledky vyhledávání - fuzzy C-regression clustering

  1. 1

    T–S fuzzy model identification based on a novel fuzzy c-regression model clustering algorithm Autor Li, Chaoshun, Zhou, Jianzhong, Xiang, Xiuqiao, Li, Qingqing, An, Xueli

    ISSN: 0952-1976, 1873-6769
    Vydáno: Elsevier Ltd 01.06.2009
    “…This paper proposes a novel approach for identification of Takagi–Sugeno (T–S) fuzzy model, which is based on a new fuzzy c-regression model (FCRM…”
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  2. 2

    A type-2 fuzzy c-regression clustering algorithm for Takagi–Sugeno system identification and its application in the steel industry Autor Fazel Zarandi, M.H., Gamasaee, R., Turksen, I.B.

    ISSN: 0020-0255, 1872-6291
    Vydáno: Elsevier Inc 15.03.2012
    Vydáno v Information sciences (15.03.2012)
    “…This paper proposes a new type-2 fuzzy c-regression clustering algorithm for the structure identification phase of Takagi–Sugeno (T–S) systems…”
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  3. 3

    Incremental Fuzzy C-Regression Clustering From Streaming Data for Local-Model-Network Identification Autor Blazic, Saso, Skrjanc, Igor

    ISSN: 1063-6706, 1941-0034
    Vydáno: New York IEEE 01.04.2020
    Vydáno v IEEE transactions on fuzzy systems (01.04.2020)
    “…In this paper, a new approach to evolving fuzzy model identification from streaming data is given…”
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  4. 4

    Hybrid System Identification by Incremental Fuzzy C-regression Clustering Autor Blazic, Saso, Skrjanc, Igor

    ISSN: 1558-4739
    Vydáno: IEEE 01.07.2020
    “…In this paper, an approach to the identification of hybrid systems is discussed. It is based on the incremental fuzzy C-regression clustering…”
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  5. 5

    Fuzzy Ordered c-Means Clustering and Least Angle Regression for Fuzzy Rule-Based Classifier: Study for Imbalanced Data Autor Leski, Jacek M., Czabanski, Robert, Jezewski, Michal, Jezewski, Janusz

    ISSN: 1063-6706, 1941-0034
    Vydáno: IEEE 01.11.2020
    Vydáno v IEEE transactions on fuzzy systems (01.11.2020)
    “…This article introduces a new classifier design method that is based on a modification of the traditional fuzzy clustering…”
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  6. 6

    A fuzzy Cregression model algorithm using a new PSO algorithm Autor Taieb, Adel, Soltani, Moez, Chaari, Abdelkader

    ISSN: 0890-6327, 1099-1115
    Vydáno: Bognor Regis Wiley Subscription Services, Inc 01.01.2018
    “…‐output local linear Takagi‐Sugeno fuzzy models using the weighted recursive least squares (WRLS…”
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  7. 7

    Interacting T-S fuzzy particle filter algorithm for transfer probability matrix of adaptive online estimation model Autor Wang, Xiao-li, Xie, Wei-xin, Li, Liang-qun

    ISSN: 1051-2004, 1095-4333
    Vydáno: Elsevier Inc 01.03.2021
    Vydáno v Digital signal processing (01.03.2021)
    “…•The proposed algorithm can be regarded as a switching dynamical model.•A fuzzy C-regression clustering method based on maximum correntropy principle and spatial-temporal information is proposed…”
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  8. 8

    Fuzzy C-Regression Clustering Algorithm Based 7-Dof Redundant Manipulators Inverse Dynamics Control Autor Zhao, Jiayu, Zhao, Tao, Yang, Hainan, Pan, Jianhui

    ISSN: 1948-9447
    Vydáno: IEEE 16.05.2025
    “… To address this challenge, we propose a data-driven approach using the interval type-2 (IT2) fuzzy c-regression clustering…”
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  9. 9

    Dynamic Type-2 Fuzzy Dependent Dirichlet Regression Mixture clustering model Autor Gamasaee, R., Zarandi, M.H. Fazel

    ISSN: 1568-4946, 1872-9681
    Vydáno: Elsevier B.V 01.08.2017
    Vydáno v Applied soft computing (01.08.2017)
    “…) technique and Interval Type-2 Fuzzy C-regression Clustering Model (IT2FCRM). DDPM method demonstrates that the probability of assigning data to a cluster including…”
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  10. 10

    A novel recursive T-S fuzzy semantic modeling approach for discrete state-space systems Autor Li, Liang-Qun, Wang, Xiao-Li, Xie, Wei-Xin, Liu, Zong-Xiang

    ISSN: 0925-2312, 1872-8286
    Vydáno: Elsevier B.V 07.05.2019
    Vydáno v Neurocomputing (Amsterdam) (07.05.2019)
    “…•Fuzzy correntropy is constructed.•A novel kernel fuzzy C-regression model clustering based on fuzzy correntropy is proposed…”
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  11. 11

    Rule-based fuzzy neural networks realized with the aid of linear function Prototype-driven fuzzy clustering and layer Reconstruction-based network design strategy Autor Park, Sang-Beom, Oh, Sung-Kwun, Kim, Eun-Hu, Pedrycz, Witold

    ISSN: 0957-4174, 1873-6793
    Vydáno: Elsevier Ltd 01.06.2023
    Vydáno v Expert systems with applications (01.06.2023)
    “… The LFPFC constitutes a new clustering technique inspired by the fuzzy c-regression model (FCRM…”
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  12. 12

    Interval type-2 modified fuzzy c-regression model clustering algorithm in TS Fuzzy Model identification Autor Dam, Tanmoy, Deb, Alok Kanti

    Vydáno: IEEE 01.07.2016
    “…This paper introduces an interval type-2 modified fuzzy c-regression model (IT2MFCRM) clustering algorithm for identifying the structure in TS Fuzzy Model…”
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  13. 13

    Fuzzy weighted c-harmonic regressions clustering algorithm Autor Zhao, Yang, Wang, Pei-hong, Li, Yi-guo, Li, Meng-yang

    ISSN: 1432-7643, 1433-7479
    Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2018
    Vydáno v Soft computing (Berlin, Germany) (01.07.2018)
    “…As a well-known regression clustering algorithm, fuzzy c -regressions (FCR) has been widely studied and applied in various areas…”
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  14. 14

    Block Sparse Representations in Modified Fuzzy C-Regression Model Clustering Algorithm for TS Fuzzy Model Identification Autor Dam, Tanmoy, Deb, Alok

    ISBN: 1479975605, 9781479975600
    Vydáno: IEEE 01.12.2015
    “… This noise clustering concept has been taken into the proposed objective function to obtain the fuzzy partition matrix of product space data…”
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  15. 15

    Hinging hyperplane based regression tree identified by fuzzy clustering and its application Autor Kenesei, Tamás, Abonyi, János

    ISSN: 1568-4946, 1872-9681
    Vydáno: Elsevier B.V 01.02.2013
    Vydáno v Applied soft computing (01.02.2013)
    “… A novel tool for regression tree identification is proposed based on the synergistic combination of fuzzy c-regression clustering and the concept of hierarchical modeling. In a special case (c=2…”
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  16. 16

    基于改进模糊C回归聚类的水轮发电机组的模糊辨识 Autor 罗红俊, 马龙, 张官祥, 魏春阳, 陈绪鹏, 金学铭, 李超顺

    ISSN: 1007-2284
    Vydáno: 中国长江电力股份有限公司白鹤滩电厂,四川凉山 615400%华中科技大学土木与水利工程学院,武汉 430074%长江三峡能事达电气股份有限公司,武汉 430000 15.09.2021
    Vydáno v 中国农村水利水电 (15.09.2021)
    “…TK730; 针对水轮发电机组精确建模的难题,提出了一种基于改进模糊C回归聚类的T-S模糊模型辨识方法.考虑到样本输出值与聚类超平面输出之间的误差值指标的重要性,对于模糊C回归聚类算法进行了改进.该算法将误差值的倒数赋给对应的样本隶属度,构建新的权重矩阵用于更新聚类超平面,从而加速聚类朝向最优聚类超平面的收敛;提出一个…”
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  17. 17

    Intuitionistic fuzzy C-regression by using least squares support vector regression Autor Lin, Kuo-Ping, Chang, Hao-Feng, Chen, Tung-Lian, Lu, Yu-Ming, Wang, Ching-Hsin

    ISSN: 0957-4174, 1873-6793
    Vydáno: Elsevier Ltd 01.12.2016
    Vydáno v Expert systems with applications (01.12.2016)
    “…•The novel clustering algorithm improves conventional fuzzy c-regression model.•Empirical results indicate that the proposed clustering algorithm has superior performance…”
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  18. 18

    The estimation of the state of charge for lithium-ion battery by fuzzy c-regression model (FCRM) clustering algorithm Autor Chung-Chun Kung, Shuo-Chieh Chang

    ISBN: 9781457706523, 1457706520
    ISSN: 1062-922X
    Vydáno: IEEE 01.10.2011
    “…In this paper, the estimation of the state of charge (SOC) of lithium-ion battery by fuzzy c-regression model (FCRM…”
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  19. 19

    A modified fuzzy c-regression model clustering algorithm for T-S fuzzy model identification Autor Soltani, M, Aissaoui, B, Chaari, A, Ben Hmida, Faycal, Gossa, M

    ISBN: 9781457704130, 1457704137
    Vydáno: IEEE 01.03.2011
    “…In this paper, a modified fuzzy c-regression model (FCRM) clustering algorithm for identification of Takagi-Sugeno…”
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  20. 20

    A study of cluster validity criteria for the fuzzy c-regression models clustering algorithm Autor Chung-Chun Kung, Jui-Yiao Su

    ISBN: 142440990X, 9781424409907
    ISSN: 1062-922X
    Vydáno: IEEE 01.10.2007
    “…The fuzzy c-regression models (FCRM) clustering algorithm can fit data to locally regression models which are linear in their parameters and be used as a tool to the identification of complex nonlinear systems…”
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