Self-organizing operator maps in complex system analysis

被引：0
|
作者：
Lehtimäki, P [1 ]
Raivio, K [1 ]
Simula, O [1 ]
机构：
[1] Aalto Univ, Lab Comp & Informat Sci, FIN-02015 Espoo, Finland
来源：
ARTIFICIAL NEURAL NETWORKS AND NEURAL INFORMATION PROCESSING - ICAN/ICONIP 2003 | 2003年 / 2714卷
关键词：
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：
The growth in amount of data available today has encouraged the development of effective data analysis methods to support human decision-making. Neuro-fuzzy computation is a soft computing hybridisation combining the learning capabilities of the neural networks with the linguistic representation of data provided by the fuzzy models. In this paper, a framework to build temporally local neuro-fuzzy systems for the analysis of nonstationary process data using self-organizing operator maps is described.
引用
收藏
页码：622 / 629
页数：8
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