An evolving connectionist system for data stream fuzzy clustering and its online learning

被引：12
|
作者：
Bodyanskiy, Yevgeniy V. [1 ]
Tyshchenko, Oleksii K. [1 ]
Kopaliani, Daria S. [1 ]
机构：
[1] Kharkiv Natl Univ Radio Elect, Control Syst Res Lab, 14 Nauky Ave, UA-61166 Kharkov, Ukraine
来源：
NEUROCOMPUTING | 2017年 / 262卷
关键词：
Evolving connectionist system; Neuro-fuzzy network; Data stream; Fuzzy clustering; C-MEANS; INFERENCE SYSTEM; NEURAL-NETWORK; IDENTIFICATION; CLASSIFIER; FLEXFIS; ETS;
D O I：
10.1016/j.neucom.2017.03.081
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
An evolving cascade neuro-fuzzy system and its online learning procedure are considered in this paper. The system is based on conventional Kohonen neurons. The proposed system solves a clustering task of non-stationary data streams under uncertainty conditions when data come in the form of a sequential stream in an online mode. A quality estimation process is defined by finding an optimal value of the used cluster validity index. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码：41 / 56
页数：16
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