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Optimization of Fuzzy Learning Machine by Using Particle Swarm Optimization

PSO 알고리즘을 이용한 퍼지 Extreme Learning Machine 최적화

  • Roh, Seok-Beom (Department of Electronics Convergence Engineering, Wonkwang University) ;
  • Wang, Jihong (Department of Electronics Convergence Engineering, Wonkwang University) ;
  • Kim, Yong-Soo (Department of Computer Engineering, Daejon University) ;
  • Ahn, Tae-Chon (Department of Electronics Convergence Engineering, Wonkwang University)
  • 노석범 (원광대학교 전자융합공학과) ;
  • 왕계홍 (원광대학교 전자융합공학과) ;
  • 김용수 (대전대학교 컴퓨터공학과) ;
  • 안태천 (원광대학교 전자융합공학과)
  • Received : 2015.11.20
  • Accepted : 2016.01.14
  • Published : 2016.02.25

Abstract

In this paper, optimization technique such as particle swarm optimization was used to optimize the parameters of fuzzy Extreme Learning Machine. While the learning speed of conventional neural networks is very slow, that of Extreme Learning Machine is very fast. Fuzzy Extreme Learning Machine is composed of the Extreme Learning Machine with very fast learning speed and fuzzy logic which can represent the linguistic information of the field experts. The general sigmoid function is used for the activation function of Extreme Learning Machine. However, the activation function of Fuzzy Extreme Learning Machine is the membership function which is defined in the procedure of fuzzy C-Means clustering algorithm. We optimize the parameters of the membership functions by using optimization technique such as Particle Swarm Optimization. In order to validate the classification capability of the proposed classifier, we make several experiments with the various machine learning datas.

Acknowledgement

Supported by : 원광대학교

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