Ten years of genetic fuzzy systems: current framework and new trends

Author

Cordón, O. ; Herrera, E. ; Gomide, E. ; Hoffman, E. ; Magdalena, L.

Author_Institution

Dept. Comput. Sci. & A.I., Granada Univ., Spain

Volume

3

fYear

2001

fDate

25-28 July 2001

Firstpage

1241

Abstract

Although fuzzy systems demonstrated their ability to solve different kinds of problems in various applications, there is an increasing interest on augmenting them with learning capabilities. Two of the most successful approaches to hybridise fuzzy systems with adaptation methods have been made in the realm of soft computing: neuro-fuzzy systems and genetic fuzzy systems hybridise the approximate reasoning method of fuzzy systems with the learning capabilities of neural networks and evolutionary algorithms. The article focuses on genetic fuzzy systems, paying special attention to genetic fuzzy rule based systems, giving a brief overview of the field

Keywords

adaptive systems; bibliographies; fuzzy neural nets; fuzzy systems; genetic algorithms; inference mechanisms; knowledge based systems; learning (artificial intelligence); uncertainty handling; adaptation methods; approximate reasoning method; evolutionary algorithms; genetic fuzzy rule based systems; genetic fuzzy systems; learning capabilities; neural networks; neuro-fuzzy systems; soft computing; Biological cells; Computer networks; Computer science; Evolutionary computation; Fuzzy neural networks; Fuzzy reasoning; Fuzzy systems; Genetic algorithms; Knowledge based systems; Neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th

Conference_Location

Vancouver, BC

Print_ISBN

0-7803-7078-3

Type

conf

DOI

10.1109/NAFIPS.2001.943725

Filename

943725