Model identification of a servo-tracking system using fuzzy clustering

被引：0

作者：

Nguyen, EM ^{[1
]}

Prasad, NR ^{[1
]}

机构：

[1] New Mexico State Univ, Klipsch Sch Elect & Comp Engn, Las Cruces, NM 88003 USA

来源：

INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS | 1999年 / 7卷 / 04期

关键词：

system identification; fuzzy clustering;

D O I：

10.1142/S0218488599000295

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper investigates the use of Fuzzy Clustering as a means for model identification of a complex and highly non-linear servo-tracking system when only observational data is available. The use of Fuzzy Clustering facilitates automatic generation of rules and its antecedent parameters. The consequent of the model is then formulated in the form of Takagi, Sugeno and Kang (TSK), and its parameters determined by the Least Squares Method (LSM).

引用

页码：337 / 346

页数：10

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