A new approach to fuzzy modeling

被引：330

作者：

Kim, E ^{[1
]}

Park, M ^{[1
]}

Ji, SW ^{[1
]}

Park, M ^{[1
]}

机构：

[1] SEOUL NATL POLYTECH UNIV,DEPT ELECT ENGN,SEOUL 139743,SOUTH KOREA

来源：

IEEE TRANSACTIONS ON FUZZY SYSTEMS | 1997年 / 5卷 / 03期

关键词：

fuzzy C-regression model; fuzzy modeling; gradient descent;

D O I：

10.1109/91.618271

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 proposes a new approach to fuzzy modeling. The suggested fuzzy model can express a given unknown system with a few fuzzy rules as well as Takagi and Sugeno's model [1], because it has the same structure as that of Takagi and Sugeno's model, It is also as easy to implement as Sugeno and Yasuhawa's model [2] because its identification mimics the simple identification procedure of Sugeno and Yasukawa's model. The suggested algorithm is composed of two steps: coarse tuning and fine tuning, In coarse tuning, fuzzy C-regression model (FCRM) clustering is: used [3], which is a modified version of fuzzy C-means (FCM) [4], In fine toning, gradient descent algorithm is used to precisely adjust parameters of the fuzzy model instead of nonlinear optimization methods used in other models, Finally, some examples are given to demonstrate the validity of this algorithm.

引用

页码：328 / 337

页数：10

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