Neuro-fuzzy networks in time series modelling

被引：0
|
作者：
Gorzalczany, MB [1 ]
Gluszek, A [1 ]
机构：
[1] Kielce Univ Technol, Dept Elect & Comp Engn, PL-25314 Kielce, Poland
来源：
KES'2000: FOURTH INTERNATIONAL CONFERENCE ON KNOWLEDGE-BASED INTELLIGENT ENGINEERING SYSTEMS & ALLIED TECHNOLOGIES, VOLS 1 AND 2, PROCEEDINGS | 2000年
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 paper briefly presents and compares four neuro-fuzzy systems used for rule-based modelling of dynamic processes (chaotic Mackey-Glass time series). The following systems have been considered: nfMod - the system proposed in this paper, the well-known ANFIS and NFIDENT systems, and an alternative neuro-fuzzy system reported in literature. The main criterion of comparison of all systems is their performance (the accuracy of modelling) versus interpretability (the transparency and the ability to explain generated decisions; it also includes an analysis and pruning of obtained fuzzy-rule bases).
引用
收藏
页码：450 / 453
页数：4
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