Earticle

초록

영어

In this paper, we introduce the design of fuzzy respective space-based neuro-fuzzy networks for pattern recognition. The proposed networks are realized by partitioning of the fuzzy respective input space to generate the fuzzy rules. The respectively partitioned spaces using fuzzy respective input space express the rules of the networks. The consequence part of the rules is represented by polynomial functions. The coefficients of consequence part of the rules are learned by the back-propagation algorithm. And we also optimize the proposed networks using real-coded genetic algorithms. A numerical example is given to evaluate the validity of the proposed networks for pattern recognition. As a result, this paper shows that the proposed networks have the good result together with fewer rules.

목차

Abstract
 1. Introduction
 2. Design of the Respective Space-based NFNs
  2.1. The Structure of the Respective Space-based NFNs
  2.2. The Learning Algorithm
 3. Genetic Optimization of the Proposed NFNs
 4. Experimental Studies
 5. 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