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.

引用

页码：113 / 116

页数：4

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