Self-organizing neural fuzzy inference network for intelligent control

被引：0
|
作者：
Constantin, N [1 ]
Dumitrache, I [1 ]
Mihu, I [1 ]
机构：
[1] Univ Politehnica Bucharest, Control & Syst Engn Dept, Bucharest 77206, Romania
来源：
PROCEEDINGS OF THE 2000 IEEE INTERNATIONAL SYMPOSIUM ON INTELLIGENT CONTROL | 2000年
关键词：
D O I：
10.1109/ISIC.2000.882953
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A self-organizing neural fuzzy inference network (SONFIN) is proposed in this paper. This network is built for realizing the basic elements and functions of dynamic fuzzy inference, considered like a series of dynamic fuzzy rules. Each node and weight represent an element in a fuzzy rule. These are created on-line based on a concurrent structure and parameter identification. The structure learning together with the parameter learning forms the learning algorithms for a dynamic neural fuzzy network. The aligned clustering-based algorithm is used for reducing the number of generated fuzzy rules. Efficiency of the SONFIN is verified from the simulation results.
引用
收藏
页码：375 / 379
页数：5
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