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

页码：169 / 172

页数：4

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