A fuzzy classifier based on Mamdani fuzzy logic system and genetic algorithm

Author

Weihong, Zhou ; Shunqing, Xiong ; Ting, Ma

Author_Institution

Nat. Astron. Obs., Chinese Acad. of Sci., Beijing, China

fYear

2010

fDate

28-30 Nov. 2010

Firstpage

198

Lastpage

201

Abstract

Most of the fuzzy classifiers are created by fuzzy rules based on apriori knowledge and expert´s knowledge, but in many applications, it´s difficult to obtain fuzzy rules without apriori knowledge of the data. To solve this problem, a new way of creating Mamdani fuzzy classifier based on Mamdani fuzzy logical system is proposesed in this paper, and the new fuzzy classifier is improved with the genetic algorithm further. The result of data simulation with Iris data shows the new Mamdani fuzzy classifier has minimum number of features, minimum number of fuzzy rules and better precision.

Keywords

fuzzy logic; fuzzy set theory; fuzzy systems; genetic algorithms; pattern classification; Mamdani fuzzy logic system; apriori knowledge; data simulation; expert´s knowledge; fuzzy classifier; fuzzy rules; genetic algorithm; iris data; Accuracy; Classification algorithms; Data models; Fuzzy logic; Fuzzy systems; Iris; Presses; Mamdani fuzzy logical system; fuzzy classifier; fuzzy reasoning; genetic algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Computing and Telecommunications (YC-ICT), 2010 IEEE Youth Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-8883-4

Type

conf

DOI

10.1109/YCICT.2010.5713079

Filename

5713079