Implementation of evolutionary fuzzy systems
Yuhui Shia b, Russell C. Eberharta c, Yaobin Chena c
- aPurdue School of Engineering and Technology, Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA
- bSch. of Eng. & Technol., Purdue Univ., West Lafayette, IN, USA#TAB#
- cPurdue School of Engineering and Technology, Indiana University—Purdue University Indianapolis, Indianapolis, IN, USA
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Abstract
Evolutionary fuzzy systems are discussed in which the membership function shapes and types and the fuzzy rule set including the number of rules inside it are evolved using a genetic (evolutionary) algorithm. In addition, the genetic parameters (operators) of the evolutionary algorithm are adapted via a fuzzy system. Benefits of the methodology are illustrated in the process of classifying the iris data set. Possible extensions of the methods are summarized.
TL;DRBenefits of the methodology are illustrated in the process of classifying the iris data set and possible extensions of the methods are summarized. (Semantic Scholar)