FURL-A theory revision approach to learning fuzzy rules

Author

Rozich, Ryan ; Ioerger, Thomas ; Yager, Ronald

Author_Institution

Dept. of Comput. Sci., Texas A&M Univ., College Station, TX, USA

Volume

1

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

791

Lastpage

796

Abstract

Fuzzy rules have been shown to be very useful in modeling relationships between variables that have a high degree of uncertainty or ambiguity. A major question in regards to learning fuzzy rule bases is how to handle interactions between rules of overlapping coverage. Structures, such as Yager´s (1993, 1998) HPS (hierarchical prioritized structure), have been proposed to answer this question. In this paper, we present our system of learning a hierarchical fuzzy rule base named FURL (for Fuzzy Rule Learner). FURL takes advantage of the properties of HPS to learn hierarchical levels of fuzzy rules. FURL applies machine-learning techniques from theory revision such as credit assignment and repair to fuzzy rules. We also discuss the results of testing FURL on multiple benchmark datasets and finally discuss our results

Keywords

fuzzy logic; knowledge acquisition; learning (artificial intelligence); uncertainty handling; FURL; Fuzzy Rule Learner; HPS; ambiguity; benchmark datasets; credit assignment; hierarchical fuzzy rule base; hierarchical prioritized structure; machine-learning techniques; relationship modeling; theory revision; theory revision approach; uncertainty; Benchmark testing; Computer science; Control systems; Databases; Expert systems; Fuzzy control; Fuzzy sets; Fuzzy systems; Hybrid intelligent systems; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2002. FUZZ-IEEE'02. Proceedings of the 2002 IEEE International Conference on

Conference_Location

Honolulu, HI

Print_ISBN

0-7803-7280-8

Type

conf

DOI

10.1109/FUZZ.2002.1005094

Filename

1005094