• ETRI Journal
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• v.27 no.3
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• pp.257-266
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• 2005

Many multi-modulus blind equalization algorithms (MMA) have been presented in the past to overcome the undesirable high misadjustment exhibited by the well-known constant modulus algorithm. Some of these MMA schemes, specifically tailored for quadrature amplitude modulation (QAM) constellations, have also been proved to fix the phase offset error without needing any rotator at the end of the equalizer stage. In this paper, a new multi-modulus algorithm is presented for QAM signals. The contribution lies in the technique to incorporate the sliced symbols (outcomes of decision device) in the multi-modulus-based weight adaptation process. The convergence characteristics of the proposed sliced multi-modulus algorithm (S-MMA) is demonstrated by way of simulations, and it is shown that it gives better steady-state performance in terms of residual inter-symbol interference and symbol-error rate. It has also been shown that the proposed algorithm exhibits lesser steady-state misadjustment compared to the best reported MMA.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.99-102
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• 2001

The brief proposes to reduce the misadjustment of the adaptive filter coefficients that trace the interference signal in the sidelobe. The proposed sidelobe canceller that has the form of Griffiths-Jim sidelobe canceller with an adaptive compensator that reduces the misadjustment. The proposed sidelobe canceller updates the filter coefficients by the error Signet of the adaptive compensator instead of the output signal. This brief shows the Improvement of the performance by comparing the computer simulation of the output signal of the Griffiths-Jim sidelobe canceller to the output signal of the proposed sidelobe canceller.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.5
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• pp.1172-1179
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• 1996

General adaptive noise canceller(ANC) suffers from the misadjustment of adaptive filter weights, because of the gradient-estimate noise at steady state. In this paper, an adaptive noise cancellation algorithm with speech detector which is distinguishing speech from silence and adaptation-transient region is proposed. The speech detector uses property of adaptive prediction-error filter which can filter the highly correlated speech. To detect speech region, estimation error which is the output of the adaptive filter is applied to the adaptive prediction-error filter. When speech signal apears at the input of the adaptive prediction-error filter. The ratio of input and output energy of adaptive prediction-error filter becomes relatively lower. The ratio becomes large when the white noise appears at the input. So the region of speech is detected by the ratio. Sign algorithm is applied at speech region to prevent the weights from perturbing by output speech of ANC. As results of computer simulation, the proposed algorithm improves segmental SNR and SNR up to about 4 dBand 11 dB, respectively.

• Journal of Power Electronics
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• v.14 no.1
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• pp.175-182
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• 2014

Tracking harmonic current quickly and precisely is one of the keys to designing active power filters (APF). In the past, the current state feedback decoupling PI control was an effective means for three-phase systems in the current control of constant voltage constant frequency inverters and high frequency PWM reversible rectifiers. This paper analyzes in detail the limitation of the conventional PI conditioner in the APF application field and presents a novel PI control method. Canceling the delay of one sampling period and the misadjustment for tracking the harmonic current is the key problem of this PI control. In this PI control, the predictive output current value is obtained by a state observer. The delay of one sampling period is remedied in this digital control system by the state observer. The predictive harmonic command current value is obtained by a repetitive predictor synchronously. The repetitive predictor can achieve better predictions of the harmonic current. By this means, the misadjustment of the conventional PI control for tracking the harmonic current is cancelled. The experiment results with a three-level NPC APF indicate that the steady-state accuracy and dynamic response of this method are satisfying when the proposed control scheme is implemented.

• The Journal of Natural Sciences
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• v.8 no.2
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• pp.63-67
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• 1996

Adaptive algorithms based on the higher order error criterion such as the LVCMS and the LMF show performance degradation if input signal contains additive noise with a heavier-tailed density. Conventional analysis often neglects higher order terms in the recursion and my not suit for prediction exact behavior of these higher order algorithms. This paper presents a new convergence analysis which contains all the higher order term in the recursion. The analysis shows that the higher order terms, which are often neglected, dose not affect the upper bound on the step size but the misadjustment. However, the effect decreases sharply proportional to the square of the step size.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.51-56
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• 2019

This paper related with the performance comparison of CCA and RMMA blind adaptive equalization in order to reduce the intersymbol interference which is occurred in channel when transmitting the 16-QAM signal, high spectrum efficiencies of nonconstant modulus characteristic. The CCA possible to improve the misadustment and initial convergence by compacting the every signal constellation of 16 by using the sliced symbol of the decision device output, namely statistical symbol, but incresing the computational cost. The RMMA possible to minimize the fast convergence speed and misadjustment and channel tracking capability without increasing the computational cost by obtain the error signal after transform to 4 constant modulus signal based on the region of signal constellation located. In this paper, these algorithm were implemented in the same channel, and the blind adaptive equalization performance were compared using the equalizer output signal constellation, residual isi, MSE, SER. As a result of simulation, the RMMA has better performance in output signal constellation, residual isi and MSE compared to the CCA, but has slow convergence speed about 1.3 times. And the SER performance presenting the robustness to the noise signal, the CCA has more beeter in less SNR, but the RMMA has better in greater than 6dB in SNR.

• The Journal of the Acoustical Society of Korea
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• v.27 no.2E
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• pp.64-68
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• 2008

This paper investigates noise reduction performance and performs convergence analysis of a Variable Error Data Normalized Step-Size Least Mean Square(VEDNSS LMS) algorithm. Adopting VEDNSS LMS results in higher system complexity, but noise is reduced providing fast convergence speed Mathematical analysis demonstrates that tap coefficient misadjustment converges. This is confirmed by computer simulation with the proposed algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.224-227
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• 1993

In this paper, a new LMS algorithm with a variable step size (VVS LMS) is presented. The change of step size .mu. at each iteration, which increases or decreases according to the misadaptation degree, is computed by a proportional fuzzy logic controller. As a result the algorithm has very good convergence speed and low steady-state misadjustment. The norm of the cross correlation between the estimation error and input signal is used. As a measure of the misadaptation degree. Simulation results are presented to compare the performance of the VSS LMS algorithm with the normalized LMS algorithm.

• Journal of Industrial Technology
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• v.13
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• pp.33-41
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• 1993

In this paper, a new LMS algorithm with a fuzzy variable step size (FVS LMS) is presented. The change of step size ${\mu}$, at each iteration which is increases or decreases according to the misadaptation degree, is computed by a proportional fuzzy logic controller. As a result the algorithm has very good convergence speed and low steady-state misadjustment. As a measure of the misadaptation degree, the norm of the cross correlation between the estimation error and input signal is used. Simulation results are presented to compare the performance of the FVSS LMS algorithm with the normalized LMS algorithm.