Paper Info
Title
Genetic-Fuzzy modeling on high dimensional spaces
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.
Year
DOI
Venue
2006
10.1007/11892960_138
KES (1)
Keywords
Field
DocType
optimal structure,new representation method,input dimension,input space,fuzzy system,explosive increase,automatic design,search space,high dimensional space,genetic-fuzzy modeling,fuzzy rule
Neuro-fuzzy,Fuzzy classification,Defuzzification,Fuzzy set operations,Computer science,Fuzzy logic,Artificial intelligence,Fuzzy number,Fuzzy associative matrix,Fuzzy rule
Conference
Volume
ISSN
ISBN
4251
0302-9743
3-540-46535-9
Citations 
PageRank 
References 
0
0.34
2
Authors
2
Name
Order
Citations
PageRank
Joon-Min Gil126537.38
SeongHoon Lee2107.08