• The Journal of the Acoustical Society of Korea
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• v.28 no.8
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• pp.815-821
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• 2009

Information processing in variable and noisy environments is usually accomplished by means of adaptive filters. Among various adaptive algorithms, Least Mean Square (LMS) has become the most popular for its robustness, good tracking capabilities and simplicity, both in terms of computational load and easiness of implementation. In practical application of the LMS algorithm, the most important key parameter is the Step Size. As is well known, if the Step Size is large, the convergence rate of the algorithm will be rapid, but the steady state mean square error (MSE) will increase. On the other hand, if the Step Size is small, the steady state MSE will be small, but the convergence rate will be slow. Many researches have been proposed to alleviate this drawback by using a variable Step Size. In this paper, a new variable Step Size LMS(VSSLMS) called Categorized VSSLMS (CVSSLMS) is proposed. CVSSLMS updates the Step Size by categorizing the current status of the gradient, hence significantly improves the convergence rate. The performance of the proposed algorithm was verified from the view point of convergence rate, Excessive Mean Square Error(EMSE), and complexity through experiments.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.115-118
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• 2000

We present a new variable step size LMS algorithm using the correlation between reference input and error signal of adaptive filter. The proposed algorithm updates each weight of filter by different step size at same sample time. We applied this algorithm to adaptive multip]e-notch filter. Simulation results are presented to compare the performance of the proposed algorithm with the usual LMS algorithm and another variable step algorithm.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.1
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• pp.20-25
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• 2011

In subband structure, the fullband AP adaptive filter with P projection dimension can be decomposed P adaptive sub-filters by applying maximally polyphase decomposition and noble identity. Each adaptive sub-filter has a simple weight update formula with the unit projection dimension. This subband decomposition method is one of the most practical solution in the viewpoint of implementation. For utilization in many applications, it is necessary that analysis for the optimum step size of the maximally polyphase decomposed subband AP(MPDSAP) adaptive filter. In this paper, we present an improved analysis model of mean square error and induce the initial optimum step size for the MPDSAP adaptive filter. Computer simulations show that there is a relatively good match between theory and practice for the improved analysis model of MSE and the induced initial optimum step size.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.2065-2069
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• 2004

This paper presents an adaptive equalizer using finite impulse response (FIR) filter and least-mean square (LMS) algorithm. Herein, the variable step-size technique (VSLMS) for compensating the amplitude of chrominance signal is utilized. The proposed equalizer can be enhanced and compressed the chrominance signal at color subcarrier. The LMS algorithm employed in simplicity structure but gives slow convergence speed. Thus, the variable step-size is very attractive algorithm due to its computational efficiencies and the speed of convergence is improved. In addition, experimental results are carried out by using the modulated 20T sine squared test signal. It is shown here that the adaptive equalizer can be equalized the amplitude chrominance distortion in color television transmission without relative delay distortion.

• Journal of Korea Multimedia Society
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• v.8 no.6
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• pp.753-760
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• 2005

This paper introduces a variable step-size adaptive algorithm for blind source separation. From the frequency characteristics of mixed input signals, we need to adjust the convergence speed regularly in each frequency bin. This algorithm varies a step-size according to the magnitude of input at each frequency bin. This guarantee of the regular convergence in each frequency bin would become more efficient in separation performances than conventional fixed step-size FDICA. Computer simulation results show the improvement of about 5 dB in signal to interference ratio (SIR) and the better separation quality.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.191-194
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• 2000

Frequency domain adaptive filter is effective to communication fields of many computational requirements. In this paper we propose a new variable step size algorithms which improves the convergence speed and reduces computational complexity for frequency domain adaptive filter. we compared MSE of the proposed algorithms with one of normalized FLMS using computer simulation of adaptive noise canceler based on synthesis speech.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.91-94
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• 2001

Recently a constant-norm constraint equation-error method was proposed to solve the bias problem in adaptive IIR filtering. However, the method adopts a fixed step-size and thus results in slow convergence for a small step-size and significant misadjustment error for a largestep-size. In this paper, we propose a variable step-size (VSS) algorithm that greatly improves convergence properties of the constant-norm constraint equation-error method. The analysis and the simulation results show that the proposed method indeed achieves both fast convergence and small misadjustment error.

• 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.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.3
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• pp.79-88
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• 1999

One of the most popular algorithm in adaptive signal processing is the least mean square(LMS) algorithm. The majority of these papers examine the LMS algorithm with a constant step size. The choice of the step size reflects a tradeoff between misadjustment and the speed of adaptation. Subsequent works have discussed the issue of optimization of the step size or methods of varying the step size to improve performance. However there is as yet no detailed analysis of a variable step size algorithm that is capable of giving both the speed of adaptation and convergence. In this paper we propose a new variable step size algorithm where the step size adjustment is controlled by square of the prediction error. The simulation results obtained using the new algorithm about noise canceller system and system identification are described. They are compared to the results obtained for other variable step size algorithm. function.