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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.

본 논문에서는 일반적인 신경회로망의 단점인 느린 학습속도를 획기적으로 개선한 네트워크인 Extreme Learning Machine과 전문가들의 언어적 정보들을 기술 할 수 있는 퍼지 이론을 접목한 퍼지 Extreme Learning Machine을 최적화하기 위하여 Particle Swarm Optimization 알고리즘을 이용하였다. 퍼지 Extreme Learning Machine의 활성화 함수를 일반적인 시그모이드 함수를 사용하지 않고, 퍼지 C-Means 클러스터링 알고리즘의 활성화 레벨 함수를 이용하였다. Particle Swarm Optimization 알고리즘과 같은 최적화 알고리즘을 통하여 퍼지 Extreme Learning Machine의 활성화 함수의 파라미터들을 최적화 한다. Particle Swarm Optimization과 같은 최적화 알고리즘을 통한 제안된 모델의 최적화 하고 최적화된 모델의 분류성능을 평가하기 위하여 다양한 머신 러닝 데이터 집합을 사용하여 평가한다.

Keywords

References

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