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- W2551641905 abstract "일반적으로 RLS 알고리즘에서 비정재성(non-stationary) 환경에서 시간에 따라 변하는 파라메터를 좀 더 잘 추정하기 위해서 가변 망각인자를 사용한다. RLS 알고리즘에서 가변 망각인자를 사용할 때는 연산량이 많이 증가하는 단점이 수반된다. 본 논문에서는 연산량이 적은 가우스 뉴턴 가변망각인자 RLS 알고리즘을 제안한다. 본 방법은 기존 가우스 뉴턴 가변망각인자 RLS와 거의 유사한 성능을 보유하고 있을 뿐만 아니라 부가로 요구되는 연산량을 <TEX>$O(N^2)$</TEX>에서 O(N)으로 줄이는 효과도 준다. In general, a variable forgetting factor is applied to the RLS algorithm for the time-varying parameter estimation in the non-stationary environments. The introduction of a variable forgetting factor to RLS needs heavy additional calculation complexity. We propose a new Gauss Newton variable forgetting factor RLS algorithm which needs small amount of calculation as well as estimates the better parameters in time-varying nonstationary environment. The algorithm performs as good as the conventional Gauss Newton variable forgetting factor RLS and the required additional calculation complexity reduces from <TEX>$O(N^2)$</TEX> to O(N)." @default.
- W2551641905 created "2016-11-30" @default.
- W2551641905 creator A5022485935 @default.
- W2551641905 creator A5070082173 @default.
- W2551641905 date "2016-09-30" @default.
- W2551641905 modified "2023-10-18" @default.
- W2551641905 title "Low Complexity Gauss Newton Variable Forgetting Factor RLS for Time Varying System Estimation" @default.
- W2551641905 cites W1965328530 @default.
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- W2551641905 doi "https://doi.org/10.7840/kics.2016.41.9.1141" @default.
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