Economic turning point forecasting using neural network with weighted fuzzy membership functions

被引：0
|
作者：
Chai, Soo H. [1 ]
Lim, Joon S. [1 ]
机构：
[1] Kyungwon Univ, Coll Software, Songnam 461701, South Korea
来源：
NEW TRENDS IN APPLIED ARTIFICIAL INTELLIGENCE, PROCEEDINGS | 2007年 / 4570卷
关键词：
fuzzy neural network; rule extraction; business forecasting; turning point;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper proposes a new forecasting model based on neural network with weighted fuzzy membership functions (NEWFM) concerning forecasting of turning points in business cycle by the composite index. NEWFM is a new model of neural networks to improve forecasting accuracy by using self adaptive weighted fuzzy membership functions. The locations and weights of the membership functions are adaptively trained, and then the fuzzy membership functions are combined by bounded sum. The implementation of the NEWFM demonstrates an excellent capability in the field of business cycle analysis.
引用
收藏
页码：145 / +
页数：3
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