Earticle

초록

영어

In this paper, we propose a new hybrid intelligent modeling using context clustering and Extreme Learning Machine (ELM) mechanism. It has been a sensitive issue that the ELM mechanism assigns initial parameters randomly, despite of its superior performance. The proposed approach focuses on initial parameters determination of the modeling to improve the accuracy of the ELM mechanism, through removing randomness of assignment. To accomplish it, a context clustering based on Gaussian Mixture Model (GMM), considering a relationship between input-output spaces will be adopted to a Radial Basis Function Network (RBFN) of the ELM. In addition, the proposed approach will reduce the randomness of results from the original ELM. Simulations and the results show usefulness of the proposed approach with improved performance accuracy.

목차

Abstract
 1. Introduction
 2. Related Works
  2.1. Context Clustering using Gaussian Mixture Model
  2.2. Extreme Learning Machine with Radial Basis Function Network
 3. Proposed Approach
 4. Simulation
  4.1. Simulation Environments
  4.2. Simulation and Results
  4.3. Discussion
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Junbeom Kim [ Department of Computer Science, Korea Advanced Institute of Science and Technology ]
• Wonjo Lee [ Department of Computer Science, Korea Advanced Institute of Science and Technology ]
• KyoJoong Oh [ Department of Computer Science, Korea Advanced Institute of Science and Technology ]
• Sung-Suk Kim [ Department of Computer Science, Korea Advanced Institute of Science and Technology ]
• Ho-Jin Choi [ Department of Computer Science, Korea Advanced Institute of Science and Technology ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Smart Home

• 간기

  격월간

• pISSN

  1975-4094

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605