Genetic-Fuzzy modeling on high dimensional spaces

Authors:
Joon-Min Gil;Seong-Hoon Lee
Affiliations:
Dept. of Computer Science Education, Catholic University of Daegu, Gyeongbuk, Korea;Dept. of Computer Science, Cheonan University, Cheonan, Korea
Venue:
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Year:
2006

Citing 6
Cited 0

Sugeno type controllers are universal controllers

Fuzzy Sets and Systems
Self-tuning fuzzy modeling with adaptive membership function, rules, and hierarchical structure based on genetic algorithm

Fuzzy Sets and Systems - Special issue on fuzzy neural control
An introduction to genetic algorithms

An introduction to genetic algorithms
The algorithmic beauty of plants

The algorithmic beauty of plants
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence

Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Genetic algorithms for learning the rule base of fuzzy logic controller

Fuzzy Sets and Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, in order to reduce the explosive increase of the search space as the input dimension grows, we present a new representation method for the structure of fuzzy rules, a graph structured fuzzy system. The graph structured fuzzy system can flexibly cope with the increase of the input space by selecting these fuzzy rules that significantly affects the input space among the whole set of fuzzy rules. To obtain the optimal structure and parameters of fuzzy systems, an approach to the automatic design of fuzzy systems based on L-systems is also proposed. The proposed method can efficiently construct fuzzy rules without any need for user interaction by using the rewriting mechanism of L-systems.