A FUZZY-NEURAL FILTER FOR DYNAMIC SYSTEM IDENTIFICATION

被引：0
|
作者：
Jukavicius, Vaidas [1 ]
Kazanavicius, Egidijus [1 ]
Martusevicius, Vitalijus [1 ]
机构：
[1] Kaunas Univ Technol, Dept Comp Sci, Kaunas, Lithuania
来源：
ELECTRICAL AND CONTROL TECHNOLOGIES | 2010年
关键词：
Dynamic system identification; fuzzy-neural filter; fuzzy clustering;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The paper presents a fuzzy-neural filter modelling approach for identification of dynamic systems. A fuzzy-neural filter follows the Takagi-Sugeno-Kang fuzzy inference model, whose consequent parts of fuzzy rules are implemented using recurrent neural networks comprising dynamic neurons with local output feedback. Such structure of fuzzy-neural filter enables a determination of nonlinear dynamic system parameters from input-output data of the system. The fuzzy-neural filter structure identification approach is presented and simulation results are given.
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页码：113 / 116
页数：4
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