Earticle

초록

영어

This paper introduce neuro-fuzzy networks by means of fuzzy relation space-based Rules for pattern recognizer. The proposed neuro-fuzzy networks are realized with the aid of the grid partition of the fuzzy relation input space. The partitioned spaces express the fuzzy rules of the networks. The consequence part of the rules is represented by polynomial functions whose coefficients are learned by the back-propagation algorithm. To optimize the parameters of the proposed networks, we consider real-coded genetic algorithms. The proposed networks are evaluated with the use of numerical experimentation for pattern recognizer. Finally, this paper shows that the proposed networks have the good result.

목차

Abstract
 1. Introduction
 2. Design of the fuzzy relation-based neuro-fuzzy networks
  2.1. The structure of the fuzzy relation-based NFNs
  2.2 The learning algorithm
 3. Genetic optimization
 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 Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605