Earticle

초록

영어

This paper introduces the fuzzy scatter partition-based fuzzy inference system to construct the model for nonlinear process to analyze nonlinear characteristics. The fuzzy rules of fuzzy inference systems are generated by partitioning the input space in the scatter form using Fuzzy C-Means (FCM) clustering algorithm. The premise parameters of the rules are determined by membership matrix by means of FCM clustering algorithm. The consequence part of the rules is represented in the form of polynomial functions and the parameters of the consequence part are estimated by least square errors. The proposed model is evaluated with the performance using the data widely used in nonlinear process. Finally, this paper shows that the proposed model has the good result for high-dimension nonlinear process.

목차

Abstract
 1. Introduction
 2. Design of Fuzzy Scatter Partition-based Fuzzy Inference System
  2.1. Premise Identification
 3. Experimental Studies
 4. Conclusions
 References

키워드

저자

• Keon-Jun Park [ Dept. of Information and Communication Engineering, Wonkwang University ]
• Yong-Kab Kim [ Dept. of Information and Communication Engineering, Wonkwang University ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605