• The Journal of the Acoustical Society of Korea
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• v.13 no.1E
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• pp.25-36
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• 1994

A problem of making a neural network learning self-adaptive to the training set supplied is addressed in this paper. This arises from the aspect in choice of an adequate stepsize for the update of the current weigh vectors according to the training pairs. Related issues in this attempt are raised and fundamentals in neural network learning are introduced. In comparison to the most popular back-propagation scheme, the usefulness and superiority of the proposed weight update algorithm are illustrated by examing the identification of unknown nonlinear systems only from measurements.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.67-73
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• 2013

This paper relates with the performance of VSCA adaptive equalization algorithm that is used for the minimization of the intersymbol interference due to the distortion which occurs in the time dispersive channel for the transmission of 16-QAM signal. In the conventional SCA, it is possible to compensates the amplitude and phase in the received signal that are mixed with the intersymbol interference by the constellatin dependent constant by using the 2nd order statistics of the transmitted signal. But in the VSCA, it is possible to the increase the equalization performance by adding the concept of distance adjusted approach for constellation matching. We compare the performance of VSCA and SCA algorithm by computer simulation. For this, the equalizer output signal constellation, residual isi, maximum distortion and MSE were used for perfomance comparison. It was confirmed that, the VSCA algorithm has better than the SCA in every performance index by computer simulation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.27-32
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• 2020

This paper compare the adaptive equalization performance according to the values of adaptive step variation speed for adapting in VS-CCA (Variable Stepsize-Compact Constellation Algorithm) based on nonlinear function function of error signal. The VS-CCA algorithm compacts the 16-QAM nonconstant modulus signal into the 4 groups of 4-QAM constant modulus signal constellation in quadature plane, then the error signal is generated using the constant modulus of transmitted signal statistics. The adaptive equalizer coefficient were updated in order to achieve the minimum cost function by varying step based on the nonlinear function of error signal. In this time, the instantaneous adaptive step is determined according to the value of step variation speed of nonlinear function and the different equalization performance were obtained according to the step variation speed value. The equalizer internal index and external index which represents the robustness of external noise were used for the performance comparison index. As a result of computer simulation, it was confirmed that the value of variation speed less than 1.0 give more superior in every performance index compared to the greater than 1.0 in steady state.

• Journal of the Institute of Convergence Signal Processing
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• v.16 no.2
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• pp.63-68
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• 2015

This paper proposes a robust multiple audio watermarking algorithm using improved QIM(quantization index modulation) with adaptive stepsize for different signal power and SVM(support vector machine) decoding model. The proposed algorithm embeds watermarks into both frequency magnitude response and frequency phase response using QIM. This multiple embedding method can achieve a complementary robustness. The SVM decoding model can improve detection rate when it is not sure whether the extracted data are the watermarks or not. To evaluate robustness, 11 attacks are employed. Consequently, the proposed algorithm outperforms previous multiple watermarking algorithm, which is identical to the proposed one but without SVM decoding model, in PSNR and BER. It is noticeable that the proposed algorithm achieves improvements of maximum PSNR 7dB and BER 10%.