• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.967-970
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• 2001

본 논문에서는 적응 필터의 계수 갱신에서 가변 블록을 사용하는 방법을 제안하였다. 데이터 블록의 길이는 MSE 학습곡선의 시정수에 비례하도록 하였다. 이 방법에서는 적응 필터가 정상상태로 접근함에 따라 스텝사이즈를 조정하여 필터계수 갱신의 횟수를 줄일 수 있다. 제안한 방법의 유용성을 입증하기 위한 컴퓨터모의 실험을 통해 기존의 최적 스텝사이즈 수열을 이용한 알고리듬과 가변 스텝사이즈 알고리듬과 성능을 비교하였다. 그리고 MSE 의 초기값을 최소화하는 최적 초기 스텝사이즈를 유도하였다. 유도된 최적 스텝사이즈를 가변 스텝사이즈 알고리듬에 적용, 그 성능을 평가 하였다.

• Journal of IKEEE
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• v.6 no.2 s.11
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• pp.111-118
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• 2002

In this paper, we present a variable block method to reduce additive computational requirements in determining step-size of variable step-size LMS (VS-LMS) algorithms. The block length is inversely proportional to the changing of step-size in VS-LMS algorithm. The technique reduces computational requirements of the conventional VS-LMS algorithms without a degradation of performance in convergence rate and steady state error. And a method for deriving initial step-size, when the input is zero mean, white Gaussian sequence, is proposed. For demonstrating the good performances of the proposed method, simulation results are compared with the conventional variable step-size algorithms in convergence speed and computational requirements.

• Proceedings of the KIPE Conference
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• 2018.07a
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• pp.120-122
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• 2018

본 논문은 최대 전력점을 추종하는 기존 P&O 방식의 동적 응답 특성을 향상시키기 위해 가변 스텝 사이즈를 적용한 P&O 방식 기반의 MPPT 알고리즘을 제안하였으며, 시뮬레이션 및 실험을 통해 검증하였다. 제안된 알고리즘은 듀티 제어를 통해 최대 전력점을 추종하며, 2가지 동작모드로써 가변 스텝 모드와 고속모드로 구성된다. 일사량이 일정한 경우, 가변 스텝 모드에서 듀티의 스텝 사이즈 감소를 통해 최대 전력점에서 동작점의 전압변동이 작아짐에 따라 진동이 감소하여 발전 효율이 증가한다. 일사량이 변동하는 경우, MPPT 오류를 피하기 위해 듀티와 PV 전압은 일정하게 유지하며, 일사량 변화가 끝난 시점에서 고속모드 동작을 통해 빠르게 최대 전력점으로 추종한다. 최대 전력점에 도달하면 가변 스텝 모드로 변경하여 듀티 스텝 사이즈를 감소시켜 최대 전력점을 추종한다. PV 패널, 부스트 컨버터로 구성된 PV 시스템의 시뮬레이션 및 실험을 통해 제안된 알고리즘의 타당성을 검증하였다.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.3
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• pp.1-6
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• 2016

As the convergence speed of time domain adaptive algorithm on the LMS(Least Mean Square) becomes slow when eigen value distribution width is spread, So variable step size algorithm is used widely. But it needs a lot of calculation load. In this paper we consider new algorithm, which can reduce calculations and improve convergence speed, uses instantaneous absolute value of average noise signal adapting the exponential function. For the performance of proposed algorithm is tested and simulated to system generator. As the result we show the variable step size adaptive algorithm in proportion to instantaneous absolute value is more stable and efficient than others.

• The Journal of the Acoustical Society of Korea
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• v.32 no.6
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• pp.518-524
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• 2013

In this paper, we propose a variable step size algorithm to enhance the normalized subband adaptive filter which has been proposed to improve the convergence characteristics of the conventional full band adaptive filter. The well-known Kwong's variable step size algorithm is simple, but shows better performance than that of the fixed step size algorithm. However, in case that large additive noise is present, the performance of Kwong's algorithm is getting deteriorated in proportion to the amount of the additive noise. We devised a variable step size algorithm which does not depend on the amount of additive noise by exploiting a normalized adaptation error which is the error subtracted and normalized by the estimated additive noise. We carried out a performance comparison of the proposed algorithm with other algorithms using a system identification model. It is shown that the proposed algorithm presents good convergence characteristics under both stationary and non-stationary environments.

• The Journal of the Acoustical Society of Korea
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• v.26 no.2
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• pp.69-74
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• 2007

In signal processing applications with highly correlated input signals, subband affine projection algorithm and step size controlling is a good solution for improving the slow convergence rate and large computational complexity of LMS-type algorithms. This paper proposes a subband affine projection algorithm using a variable step size. The proposed method achieves fast convergence rate and small steady-state error with a small computational complexity by combining the SAP and step size controlling in a subband structure. Experimental results on highly correlated input signal show that the proposed method is superior to the conventional methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.443-448
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• 2022

This paper presents a subband affine projection algorithm with variable step size to improve convergence performance in adaptive filtering applications with long adaptive filters and highly correlated input signals. The proposed algorithm can obtain fast convergence speed and small steady-state error by using different step sizes for each adaptive sub-filter in the subband structure to which polyphase decomposition and noble identity are applied. The step size derived to minimize the mean square error of the adaptive filter at each update time shows better convergence performance than the existing algorithm using a variable step size. In order to confirm the convergence performance of the proposed algorithm, which is superior to the existing algorithm, computer simulations are performed for mean square deviation(MSD) for AR(1) and AR(2) colored input signals considering the system identification model.

• The Journal of the Acoustical Society of Korea
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• v.28 no.3
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• pp.245-250
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• 2009

In communications and signal processing area, a number of least mean square adaptive algorithms have been used because of simplicity and robustness. However the LMS algorithm is known to have slow and non-uniform convergence. Various variable step size LMS adaptive algorithms have been introduced and researched to speed up the convergence rate. A variable step size LMS algorithm using the error difference for updating the step size is proposed. Compared with other algorithms, simulation results show that the proposed LMS algorithm has a fast convergence. The theoretical performance of the proposed algorithm is also analyzed for the steady state.

• Journal of Korea Multimedia Society
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• v.8 no.8
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• pp.1051-1056
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• 2005

Least mean square(LMS) algorithm is one of the most popular algorithm in adaptive signal processing because of the simplicity and the small computation. But the convergence speed of time domain adaptive algorithm is slow when the spread width of eigen values is wide. Moreover we have to choose the step size well for convergency in this paper, we use adaptive algorithm of wavelet transform. And we propose a new wavelet based adaptive algorithm of wavelet transform. And we propose a new wavelet based adaptive algorithm with variable step size, which Is linear to absolute value of error signal. We applied this algorithm to adaptive noise canceler. Simulation results are presented to compare the performance of the proposed algorithm with the usual algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1545-1550
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• 2021

We propose an optimal variable step size adjustment method for the simplified subband affine projection algorithm (Simplified SAP; SSAP) in a subband structure based on a polyphase decomposition technique. The proposed method provides an optimal step size derived to minimize the mean square deviation(MSD) at the time of updating the coefficients of the subband adaptive filter. Application of the proposed optimal step size in the SSAP algorithm using colored input signals ensures fast convergence speed and small steady-state error. The results of computer simulations performed using AR(2) signals and real voices as input signals prove the validity of the proposed optimal step size for the SSAP algorithm. Also, the simulation results show that the proposed algorithm has a faster convergence rate and good steady-state error compared to the existing other adaptive algorithms.