Earticle

초록

영어

We introduce a new category of fuzzy neural networks with multiple-output based on fuzzy clustering algorithm, especially, fuzzy c-means clustering algorithm (FCM-based FNNm) for pattern classification in this paper. The premise part of the rules of the proposed networks is realized with the aid of the scatter partition of input space generated by FCM clustering algorithm. The partitioned local spaces describe the fuzzy rules and the number of the partitioned local spaces is equal to the number of clusters. Due to these characteristics, we may alleviate the problem of the curse of dimensionality. The consequence part of the rules is represented by polynomial functions with multiple-output for pattern classification. And the coefficients of the polynomial functions are learned by back propagation algorithm. To optimize the parameters of the proposed FCM-based FNNm we consider real-coded genetic algorithms. The proposed networks are evaluated with the use of numerical experimentation.

목차

Abstract
 1. Introduction
 2. Design of Fuzzy c-means Clustering-based Fuzzy Neural Networks
  2.1. Fuzzy c-means Clustering Algorithm
  2.2. Structure of FCM-based FNNm
  2.3. Learning Algorithm
 3. Optimization of Networks
 4. Experimental Studies
  4.1. Iris Dataset
  4.2. WDBC Dataset
  4.3. Wine Dataset
 5. Conclusions
 References

키워드

저자

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

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605