• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.9
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• pp.1141-1145
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• 2016

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 $O(N^2)$ to O(N).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.9
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• pp.1793-1801
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• 2015

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.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.173-176
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• 2000

시변 신호 추적 특성을 향상시키기 위하여, Gauss-Newton Variable Forgetting Factor RLS (GN-VFF-RLS) Algorithm을 제안한다. 최적화된 망각인자를 가정한 기존의 RLS 알고리듬과 비교하여, 제안된 방법은 특히 신호의 변화가 급격히 일어날 경우 주목할만한 추정 성능의 향상을 보여준다. 제안된 알고리듬의 시변 추정 특성을 신호 대 잡음비와 시변 정도에 대하여 모의 실험하고 기존의 추정 알고리듬들과 비교한다.

• The Journal of the Acoustical Society of Korea
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• v.38 no.3
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• pp.283-289
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• 2019

RLS (Recursive-least-squares) algorithm is known to have good convergence and excellent error level after convergence. However, there is a disadvantage that numerical instability is included in the algorithm due to inverse matrix calculation. In this paper, we propose an algorithm with no matrix inversion to avoid the instability aforementioned. The proposed algorithm still keeps the same convergence performance. In the proposed algorithm, we adopt an averaged gradient-based step size as a self-adjusted step size. In addition, a variable forgetting factor is introduced to provide superior performance for time-varying channel estimation. Through simulations, we compare performance with conventional RLS and show its equivalency. It also shows the merit of the variable forgetting factor in time-varying channels.

• The Journal of the Acoustical Society of Korea
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• v.29 no.1
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• pp.18-23
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• 2010

The RLS (Recursive Least Squares) method is a broadly used adaptive algorithm for signal processing in electronic engineering. The RLS algorithm shows a good performance and a fast adaptation within a stationary environment, but it shows a Poor performance within a non-stationary environment because the method has a fixed forgetting factor. In order to enhance 'tracking' performances, BLS methods with an adaptive forgetting factor had been developed. This method shows a good tracking performance, however, it suffers from heavy computational loads. Therefore, we propose a modified AFF-RLS which has relatively low complexity m this paper.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.177-187
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• 2014

This paper proposes a fast subspace tracking methods, which is called GVFF FAPI, based on FAPI (Fast Approximated Power Iteration) method and GVFF RLS (Gradient-based Variable Forgetting Factor Recursive Lease Squares). Since the conventional FAPI uses a constant forgetting factor for estimating covariance matrix of source signals, it has difficulty in applying to non-stationary environments such as continuously changing DOAs of source signals. To overcome the drawback of conventioanl FAPI method, the GVFF FAPI uses the gradient-based variable forgetting factor derived from an improved means square error (MSE) analysis of RLS. In order to achieve the decreased subspace error in non-stationary environments, the GVFF-FAPI algorithm used an improved forgetting factor updating equation that can produce a fast decreasing forgetting factor when the gradient is positive and a slowly increasing forgetting factor when the gradient is negative. Our numerical simulations show that GVFF-FAPI algorithm offers lower subspace error and RMSE (Root Mean Square Error) of tracked DOAs of source signals than conventional FAPI based MUSIC (MUltiple SIgnal Classification).

• The Journal of the Acoustical Society of Korea
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• v.12 no.1E
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• pp.56-64
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• 1993

본 논문은 공간적으로 변하는 스펙트럼을 추정하는 새로운 적응 방법을 제안한다. 제안한 방법에서는 오래된 upstream의 데이터를 망각함으로서 신호의 nonstationarity를 고려해주는 시변망각인자의 개념을 recursive least square(RLS) 알고리즘에 도입하였으며, 관심이 있는 공간영역에서 탐사침을 천천히 움직여 얻은 하나의 데이터 군으로부터 downstream 스펙트럼을 추정하였다. 제시한 방법의 실현 가능성은 실제 실험(wind tunnel 이용)을 통해서 얻은 공간적으로 변하는 nonstatonary 신호의 스펙트럼을 추정하는 과정에서 입증되며 또한 기존의 방법들과 비교함으로서 그 우수성을 보인다.

• The Journal of the Acoustical Society of Korea
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• v.28 no.6
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• pp.520-525
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• 2009

The conventional studies about an adaptive beamformer assumed that the interference signals are stationary, so they used time-average of signals or Least Mean Squares. However, these methods showed low performance of canceling the non-stationary interferences. In this paper, the MAFF-RLS algorithm is developed in order to cancel non-stationary interferences, and the GSC structure using this algorithm is proposed. Furthermore, the performance of the MAFF-RLS beamformer is verified by simulation using MATLAB. This simulation results show the performance of the proposed beamformer is better than that of the SMI and the conventional RLS beamformer.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.5
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• pp.52-61
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• 1993

This paper deals with adaptive control method of a robot manipulator with one-flexible link. ARMA model is used as a prediction and estimation model, and adaptive control scheme consists of parameter estimation part and adaptive controller. Parameter estimation part estimates ARMA model's coefficients by using recursive least-squares(RLS) algorithm and generates the predicted output. Variable forgetting factor (VFF) is introduced to achieve an efficient estimation, and adaptive controller consists of reference model, error dynamics model and minimum prediction error controller. An optimal input is obtained by minimizing input torque, it's successive input change and the error between the predicted output and the reference output.