An Enhancement of a Learning Procedure in Neuro-Fuzzy Model

被引：0
|
作者：
Vlasenko, Alexander [1 ]
Vynokurova, Olena [2 ,3 ]
Vlasenko, Nataliia [4 ]
Bodyanskiy, Yevgeniy [1 ]
机构：
[1] Kharkiv Natl Univ Radio Elect, Dept Artificial Intelligence, Kharkov, Ukraine
[2] Kharkiv Natl Univ Radio Elect, Kharkov, Ukraine
[3] IT Step Univ, Lvov, Ukraine
[4] Simon Kuznets Kharkiv Natl Univ Econ, Dept Informat & Comp Engn, Kharkov, Ukraine
来源：
2018 IEEE FIRST INTERNATIONAL CONFERENCE ON SYSTEM ANALYSIS & INTELLIGENT COMPUTING (SAIC) | 2018年
关键词：
time series; neuro-fuzzy; membership function; Gaussian; Mackey-Glass; NETWORKS; SYSTEM;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In this paper we propose an extended version of the learning algorithm for the hybrid five-layer neuro-fuzzy model. Backpropagation algorithm has been applied to the first layer membership functions centers tuning. The experimental results have shown that introduced enhancement significantly improves accuracy in time-series prediction benchmark tests without significant performance cost.
引用
收藏
页码：169 / 172
页数：4
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