Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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Unsupervised Machine Learning based on Neighborhood Interaction Function for BCI(Brain-Computer Interface)

BCI(Brain-Computer Interface)에 적용 가능한 상호작용함수 기반 자율적 기계학습

  • Kim, Gui-Jung (Dept. Of Biomedical Engineering, Konyang Univ.) ;
  • Han, Jung-Soo (Division of Information & Communication, Baekseok Univ.)
  • 김귀정 (건양대학교 의공학부) ;
  • 한정수 (백석대학교 정보통신학부)
  • Received : 2015.06.27
  • Accepted : 2015.08.20
  • Published : 2015.08.28
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Abstract

This paper proposes an autonomous machine learning method applicable to the BCI(Brain-Computer Interface) is based on the self-organizing Kohonen method, one of the exemplary method of unsupervised learning. In addition we propose control method of learning region and self machine learning rule using an interactive function. The learning region control and machine learning was used to control the side effects caused by interaction function that is based on the self-organizing Kohonen method. After determining the winner neuron, we decided to adjust the connection weights based on the learning rules, and learning region is gradually decreased as the number of learning is increased by the learning. So we proposed the autonomous machine learning to reach to the network equilibrium state by reducing the flow toward the input to weights of output layer neurons.

본 연구는 비교사학습의 대표적인 방법 중 하나인 코호넨의 자기조직화 방법을 기반으로 BCI(Brain-Computer Interface)에 적용 가능한 자율적 기계학습방법을 제안한다. 이를 위해 상호작용 함수를 이용한 학습영역조정방법과 자율적 기계학습규칙을 제안하였다. 학습영역조정과 기계학습은 코호넨의 자기조직화 방법을 기반으로 한 상호작용 함수에 의한 측면제어효과를 이용하였다. 승자 뉴런을 결정하고 난 후 학습 규칙에 따라 뉴런의 연결강도를 조정하고 학습 횟수가 증가함에 따라 학습영역이 점차 감소하여 출력층 뉴런 가중치들의 입력을 향한 유동을 완화시켜 네트워크가 평형 상태(equilibrium state)에 도달하여 학습을 마칠 수 있는 자율적 기계학습을 제안하였다.

Keywords

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