• 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.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 Korean Society for Precision Engineering
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• v.12 no.7
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• pp.26-31
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• 1995

A new actuating mechanism suitable for a micro positioning device is developed using piezo-electric elements. The actuator can make a step movement of 0.5 .mu. m up to 3.5 .mu. m. The step size can be adjusted on demand. By repeating this step action, long distance movement is achieved. Precise positioning can be obtained by combining the coarse motion with the maximum step size and fine motion. Two types of fine motion have been proposed for a driving method. Firstly, feedback control bases on PID is applied. The experimental results for the two method are presented.

• 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 Institute of Internet, Broadcasting and Communication
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• v.18 no.3
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• pp.77-82
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• 2018

This paper is concerned with the performance analysis of a modified stop-and-go generalized multi-modulus algorithm (MSAG-GMMA) adaptive blind equalization algorithm with variable step size. The proposed algorithm multiplies the fixed step size by the error signal of the decision-oriented algorithm in the equalization coefficient update equation, and changes the step size according to the error size. Also, the MSAG-GMMA having a fixed step size is operated so as to maintain a fast convergence speed from a certain threshold to a steady state by determining the error signal size of the decision-directed algorithm, and when the MSAG-GMMA to work To evaluate the performance of the proposed algorithm, we use the ensemble ISI, ensemble-averaged MSE, and equalized constellation obtained from the output of the equalizer as the performance index. Simulation results show that the proposed algorithm has faster convergence speeds than MMA, GMMA, and MSAG-GMMA and has a small residual error in steady state.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.1
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• pp.104-110
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• 2009

In this paper, we suggest a new technique which employ the pipelined architecture for the implementation of the SAP adaptive filter using variable step size. According as SAP adaptive filter is sufficiently decomposed, a simplified SAP adaptive filter can be derived, and the weights of adaptive sub-filters can be updated by a simple formular without a matrix inversion. The convergence speed and the steady state error of the simplified SAP adaptive filter are improved by using variable step size. For practical implementation, the simplified SAP adaptive sub-filters are transformed by the pipeline technique.

• 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.

• 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 the Korea Academia-Industrial cooperation Society
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• v.12 no.9
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• pp.4147-4152
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• 2011

Compared to the constant modulus algorithm (CMA), the modified CMA (MCMA) is easy not only to improve the steady-state performance but also to be expanded to higher-order constellations by using fewer moduli with evenly spaced. In this paper, it is shown that the MCMA is sufficient to achieve satisfactory steady-state performance by applying a variable step-size to the MCMA without switching to an hard decision-directed algorithm. Two new methods varying the step-size are proposed, and the performance improvement of the MCMA with the new methods of variable step-size is presented as compared to the CMA and the fixed step-size MCMA through computer simulations.

• 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.