Evolutionary design of fuzzy rule base for nonlinear system modeling and control

2000 ◽  
Vol 8 (1) ◽  
pp. 37-45 ◽  
Author(s):  
Sin-Jun Kang ◽  
Chun-Hee Woo ◽  
Hee-Soo Hwang ◽  
K.B. Woo
Keyword(s):  
Nonlinear System ◽  
System Modeling ◽  
Fuzzy Rule ◽  
Evolutionary Design ◽  
Rule Base ◽  
Modeling And Control ◽  
Nonlinear System Modeling ◽  
Fuzzy Rule Base ◽  
And Control

COMPETITIVE LEARNING OF FUZZY MODELS

2002 ◽  
Vol 16 (30) ◽  
pp. 4621-4639 ◽  
Author(s):  
M. ANDRECUT ◽  
M. K. ALI
Keyword(s):  
System Modeling ◽  
Fuzzy Model ◽  
Fuzzy Rule ◽  
Competitive Learning ◽  
Structure Identification ◽  
Rule Base ◽  
Nonlinear System Modeling ◽  
Fuzzy Models ◽  
On Line ◽  
Two Stages

In general, fuzzy modeling requires two stages: structure identification (generating the fuzzy rule base) and parameter learning (optimizing parameters in fuzzy rules). Here, we present an on-line algorithm for competitive learning and optimization of fuzzy models. Differing from existing methods, in this approach the structure identification and parameter optimization of the fuzzy model can be carried out automatically, using on-line acquisition of data. We demonstrate this approach by applying it to different types of nonlinear system modeling.

Evolutionary Design of a Fuzzy Rule Base for Solving the Goal-Shooting Problem in the RoboCup 3D Soccer Simulation League

2008 ◽  
pp. 320-328
Author(s):  
Mohammad Jafar Abdi ◽  
Morteza Analoui ◽  
Bardia Aghabeigi ◽  
Ehsan Rafiee ◽  
Seyyed Mohammad Saeed Tabatabaee
Keyword(s):  
Fuzzy Rule ◽  
Evolutionary Design ◽  
Rule Base ◽  
Fuzzy Rule Base
Sign in / Sign up

Export Citation Format

Share Document