Earticle

초록

영어

In this paper, we introduce a design of fuzzy neural networks based on scatter space for nonlinear modeling. To design the networks, we partition the input space in the scatter form using fuzzy c-means (FCM) clustering algorithm which generates the fuzzy rules in the premise part of the proposed networks. The partitioned spaces express the fuzzy rules of the networks. Through this method, we are able to handle the high dimension problem. The consequence part of the rule is represented by polynomial functions whose coefficients are learned by standard back-propagation algorithm. The proposed networks are evaluated with the nonlinear process. Finally, this paper shows that the proposed networks can be utilized for high-dimension nonlinear process.

목차

Abstract
 1. Introduction
 2. Scatter Space-based Fuzzy Neural Networks
  2.1. The Structure of the Scatter Space -based FNN
  2.2. Learning Algorithm
 3. Experimental Studies
 4. Conclusion
 References

키워드

저자

• Keon-Jun Park [ Dept. of Information and Communication Engineering, Wonkwang University ]
• Dong-Yoon Lee [ Dept. of Electrical Electronic Engineering, Joongbu 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