Improving premise structure in evolving Takagi-Sugeno neuro-fuzzy classifiers

被引：22
|
作者：
Almaksour A. [1 ]
Anquetil E. [1 ]
机构：
[1] INSA de Rennes/UMR IRISA, 35043 Rennes, Avenue des Buttes de Coesmes
来源：
Evolving Systems | 2011年 / 2卷 / 1期
关键词：
Incremental learning; Neuro-fuzzy; Takagi-Sugeno;
D O I：
10.1007/s12530-011-9027-0
中图分类号：
学科分类号：
摘要：
We present in this paper a new method for the design of evolving neuro-fuzzy classifiers. The presented approach is based on a first-order Takagi-Sugeno neuro-fuzzy model. We propose a modification on the premise structure in this model and we provide the necessary learning formulas, with no problem-dependent parameters. We demonstrate by the experimental results the positive effect of this modification on the overall classification performance. © 2011 Springer-Verlag.
引用
收藏
页码：25 / 33
页数：8
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