• 한국음향학회지
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• 제38권3호
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• pp.283-289
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• 2019

RLS(Recursive-least-squares) 알고리즘은 수렴성이 좋고, 수렴 후 오차 수준도 우수한 것으로 알려져 있다. 그러나 알고리즘 내에 역행렬 계산이 포함되어 수치적 불안정성을 나타내는 단점도 있다. 본 논문에서는 언급한 불안정성을 회피하기 위해서 역행렬이 없지만 수렴성이 유사한 알고리즘을 제안한다. 이를 위해서 기울기 평균 벡터를 사용한 가변 스텝 최소 자승 알고리즘을 사용한다. 또 시변 채널 추정에 우수한 성능을 내기 위해서 계산량이 적은 가변 망각인자를 도입한다. 시뮬레이션을 통해서 기존 RLS와의 성능을 비교하고 그 유사성을 보인다. 또 시변 채널에서 가변 망각인자의 우수성도 보인다.

• 한국통신학회논문지
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• 제39C권1호
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• pp.9-16
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• 2014

본 논문에서는 이동객체의 위치인식 정확도 향상을 위하여 최소자승법을 적용한 이동객체 위치인식 보정 알고리즘을 제안하고, 성능을 분석하였다. 제안한 보정 알고리즘은 일정한 속도로 이동 중인 이동객체의 거리 값들을 TMVS (TWR Minimum Value Selection) 기법으로 측정 한 후, 이 값들을 사용하여 삼변측량법으로 이동객체의 위치를 측정하고, 최소자승법을 적용하여 위치인식 값을 보정한다. 실험결과, 시나리오 1 및 2에서 제안하는 보정알고리즘을 적용한 위치인식의 성능은 기존의 삼변측량법을 적16용한 위치인식의 성능에 비해 위치인식 정확도가 시나리오별 각각 58.84%, 40.28% 개선됨을 확인하였다.

• The Journal of the Acoustical Society of Korea
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• 제26권2E호
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• pp.63-68
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• 2007

Using the neural network model for oriented principal component analysis (OPCA), we propose a solution to the data least squares (DLS) problem, in which the error is assumed to lie in the data matrix only. In this paper, we applied this neural network model to channel equalization. Simulations show that the neural network based DLS outperforms ordinary least squares in channel equalization problems.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 추계종합학술대회 논문집
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• pp.655-658
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• 1999

In this study, We derived Recursive Least Squares(RLS) algorithm with adaptive maximum -likelihood channel estimate for digital pulse amplitude modulated sequence in the presence of intersymbol interference and additive white Gaussian noise. RLS algorithms have better convergence characteristics than conventional algorithms, LMS Least Mean Squares) algorithms.

• Communications for Statistical Applications and Methods
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• 제2권2호
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• pp.135-145
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• 1995

A new algorithm was given to successively fit the multiexponential function/nonlinear function to data by a weighted least squares method, using Gauss-Newton, Marquardt, gradient and DUD methods for convergence. This study also considers the problem of linear-nonlimear weighted least squares estimation which is based upon the usual Taylor's formula process.

• Journal of the Korean Statistical Society
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• 제29권3호
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• pp.361-373
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• 2000

A simple and efficient algorithm is introduced for generalized least squares estimation under nonnegativity constraints in the components of the parameter vector. This algorithm gives the exact solution to the estimation problem within a finite number of pivot operations. Besides an illustrative example, an empirical study is conducted for investigating the performance of the proposed algorithm. This study indicates that most of problems are solved in a few iterations, and the number of iterations required for optimal solution increases linearly to the size of the problem. Finally, we will discuss the applicability of the proposed algorithm extensively to the estimation problem having a more general set of linear inequality constraints.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권12호
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• pp.793-798
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• 2004

In this paper, the problem of adaptively identifying time-varying nonlinear systems is considered. For that purpose, the discrete time-varying Volterra series is employed as a system model, and the filtered weighted least squares (FWLS) algorithm, developed for adaptive identification of linear time-varying systems, is utilized for the adaptive identification of time-varying quadratic Volterra systems. To demonstrate the performance of the proposed approach, some simulation results are provided. Note that the FWLS algorithm, decomposing the conventional weighted basis function (WBF) algorithm into a cascade of two (i.e., estimation and filtering) procedures, leads to fast parameter tracking with low computational burden, and the proposed approach can be easily extended to the adaptive identification of time-varying higher-order Volterra systems.

• 대한전기학회논문지:시스템및제어부문D
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• 제52권4호
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• pp.233-237
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• 2003

An accurate load forecasting is essential for economics and stability power system operation. Due to high relationship between the electric power load and the electric power price, the participants of the competitive power market are very interested in load forecasting. The percentage errors of load forecasting for holidays is relatively large. In order to improve the accuarcy of load forecasting for holidays, this paper proposed load forecasting method for holidays using a fuzzy least squares linear regression algorithm. The proposed algorithm is tested for load forecasting for holidays in 1996, 1997, and 2000. The test results show that the proposed algorithm is better than the algorithm using fuzzy linear regression.

• Journal of Electrical Engineering and Technology
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• 제5권4호
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• pp.538-544
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• 2010

This paper proposes an estimation algorithm for the separate primary and secondary leakage inductances of a three phase $Y-\Delta$ transformer using least squares method. The voltage equations from the primary and secondary windings are combined into a differential equation to estimate the separate primary and secondary leakage inductances in order to use the line current of the delta winding. Separate primary and secondary leakage inductances are obtained by applying least squares method to the differential equation. The performance of the proposed algorithm is validated under transient states, such as magnetic inrush and overexcitation, as well as in the steady state with various cut-off frequencies of low-pass filter. The proposed technique can accurately generate separate leakage inductances both in the steady and transient states.

• Journal of Communications and Networks
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• 제18권1호
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• pp.8-18
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• 2016

In this paper, we propose a number of blind equalization approaches for time-varying andmulti-path channels. The approaches employ cost reference particle filter (CRPF) as the symbol estimator, and additionally employ either least mean squares algorithm, recursive least squares algorithm, or $H{\infty}$ filter (HF) as a channel estimator such that they are jointly employed for the strategy of "Rao-Blackwellization," or equally called "mixture filtering." The novel feature of the proposed approaches is that the blind equalization is performed based on direct channel estimation with unknown noise statistics of the received signals and channel state system while the channel is not directly estimated in the conventional method, and the noise information if known in similar Kalman mixture filtering approach. Simulation results show that the proposed approaches estimate the transmitted symbols and time-varying channel very effectively, and outperform the previously proposed approach which requires the noise information in its application.