The Journal of Korean Institute of Communications and Information Sciences (한국통신학회논문지)

The Korean Institute of Commucations and Information Sciences (한국통신학회)
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Kernel RLS Algorithm Using Variable Forgetting Factor

가변 망각인자를 사용한 커널 RLS 알고리즘

  • Lim, Jun-Seok (Sejong University, Dept. of Electrical Engineering) ;
  • Pyeon, Yong-Guk (GangWon Provincial College, Dept. of Information and Communication)
  • Received : 2015.06.09
  • Accepted : 2015.08.20
  • Published : 2015.09.30
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Abstract

In a recent work, kernel recursive least-squares tracker (KRLS-T) algorithm has been proposed. It is capable of tracking in non-stationary environments using a forgetting mechanism built on a Bayesian framework. The forgetting mechanism in KRLS-T is implemented by a fixed forgetting factor. In practice, however, we frequently meet that the fixed forgetting factor cannot handle time-varying system effectively. In this paper we propose a new KRLS-T with a variable forgetting factor. Experimental results show that proposed algorithm can handle time-varying system more effectively than the KRLS-T.

최근 KRLS-T라고 하는 커널 기반의 RLS 알고리즘이 제안되었다. 이 알고리즘은 비정재 환경에서 베이지안 이론에 기반한 망각 기저를 사용하고 있다. 이런 망각 기저의 핵심은 고정 망각인자를 사용하는 것이다. 그러나 실제 환경에서는 시스템의 비정재 정도까지도 시변인 경우가 많다. 이 경우 고정 망각인자로는 시스템의 바른 추정이 불가하다. 본 논문에서는 가변 망각인자를 사용한 KRLS-T를 제안한다. 그리고 모의 실험에서 가변 망각인자를 가진 KRLS-T가 시변 시스템의 추정에 더 효과적임을 보인다.

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