Identification of fuzzy systems with the aid of genetic fuzzy granulation

Authors:
Sung-Kwun Oh;Keon-Jun Park;Yong-Soo Kim;Tae-Chon Ahn
Affiliations:
Department of Electrical Engineering, The University of Suwon, Gyeonggi-do, South Korea;Department of Electrical Engineering, The University of Suwon, Gyeonggi-do, South Korea;Division of Computer Engineering, Daejeon University, Daejeon, South Korea;Department of Electrical Electronic and Information Engineering, Wonkwang University, Chon-Buk, South Korea
Venue:
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Year:
2005

Citing 5
Cited 0

Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic

Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Identification of fuzzy systems by means of an auto-tuning algorithm and its application to nonlinear systems

Fuzzy Sets and Systems
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Time Series Analysis, Forecasting and Control

Time Series Analysis, Forecasting and Control
Numerical and applicational aspects of fuzzy relational equations

Fuzzy Sets and Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose the identification of fuzzy systems with the aid of genetic fuzzy granulation to carry out the model identification of complex and nonlinear systems. The proposed fuzzy model implements system structure and parameter identification with the aid of genetic algorithms and information granulation. To identify the structure of fuzzy rules we use genetic algorithms. Granulation of information realized with Hard C-Means clustering help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms and the least square method. An example is given to evaluate the validity of the proposed model.