• The Journal of the Acoustical Society of Korea
• /
• v.12 no.1
• /
• pp.37-46
• /
• 1993

The adaptive algorithm for the Volterra filter is considered. Owing to its simplicity, the LMS algorithm for adaptive Volterra filter(AVF) is widely used as in linear adaptive filters. However, the convergence speed is unsatisfactory. For improving the convergence speed, the frequency domain LMS second order adaptive Volterra filter(FLMS-AVF) is proposed and analyzed. We show that the time and frequency domain LMS AVF's have the same steady state performance under approprate conditons. Moreover, it can be shown that this algorithm can improve the convergence speed significantly by applying self-orthogonalizing method.

• Journal of electromagnetic engineering and science
• /
• v.4 no.2
• /
• pp.93-96
• /
• 2004

In this paper we introduce a new escalator(ESC) structure-based adaptation algorithm. The proposed algorithm is independent of eigenvalues spread ratio(ESR) of channel and has faster convergence speed than that of the conventional ESC algorithms. This algorithm combines the fast adaptation ability of least square methods and the orthogonalization property of the ESC structure. From the simulation results the proposed algorithm shows superior convergence speed and no slowing down of convergence speed when we increase the ESR of the channel.

• Proceedings of the Korean Institute of Communication Sciences Conference
• /
• 1991.10a
• /
• pp.111-114
• /
• 1991

This paper proposes an efficient adaptive echo canceller using pilot filter approach to achieve improved convergence speed. The pilot filter is an adaptive filter with only a few filter coefficients to filter the received signal for the purpose of whitening the signal. Thus the convergence speed of the main LMS-TDL filter combined with the pilot filter is improved. In the proposed echo canceller, an adaptive lattice predictor as the pilot filter is used and its inverse filter is used to equalize the distorted near end talker signal. Simulation results for colored signal show that the convergence speed of the proposed echo cancellation algorithm is faster than that of the conventional LMS-TDL echo cancellation algorithm.

• The Journal of the Acoustical Society of Korea
• /
• v.28 no.7
• /
• pp.640-645
• /
• 2009

CMA (Constant Modulus Algorithm) is one of the famous algorithms in blind channel equalization. Generally, CMA converges slowly and the speed of convergence is dependent on a step-size in the CMA procedure. Many researches have tried to speed up the convergence speed by applying a variable step-size to CMA. In this paper, we propose a new CMA algorithm with improved convergence performance. The improvement comes from an orthogonal projection of an average error gradient. We show the improvement in simulation results.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.9 no.1
• /
• pp.22-33
• /
• 2005

In this paper, an efficient signal interference control technique to improve the high convergence speed of LMS algorithms is introduced in the adaptive transversal filter of DS/SS. The convergence characteristics of the proposed algorithm, whose coefficients are multiply adapted in a symbol time period by recycling the received data, is analyzed to prove theoretically the improvement of high convergence speed. According as the step-size parameter ${\mu}$ is increased, the rate of convergence of the algorithm is controlled. Also, an increase in the stop-size parameter ${\mu}$ has the effect of reducing the variation in the experimentally computed learning curve. Increasing the eigenvalue spread has the effect of controlling which is downed the rate of convergence of the adaptive equalizer. Increasing the steady-state value of the average squared error, proposed algorithm also demonstrate the superiority of signal interference control to the filter algorithm increasing convergence speed by (B+1) times due to the data-recycling LMS technique.

• Journal of the Korean Society of Industry Convergence
• /
• v.26 no.5
• /
• pp.813-823
• /
• 2023

In this study, microstructure, hardening layer hardness, and case depth were evaluated after induction hardening(IH) of base metal specimen(BM) treated with annealing and quenching-tempering specimen(QT) treated with quenching and tempering. The microstructure after IH was significantly influenced by the microstructure before IH and the induction coil heating movement speed, but the effect of the induction frequency was very small. The hardness of the hardened layer at an induction coil heating movement speed of 15 mm/s or less was more influenced by the microstructure before IH than the induction coil travel speed and induction frequency. The induction coil travel speed has the significantly effect on the case depth, the induction frequency has effect and the microstructure before IH has a small effect.

• Journal of information and communication convergence engineering
• /
• v.2 no.1
• /
• pp.36-39
• /
• 2004

In this paper, a method of improving the learning speed and convergence rate is proposed to exploit the advantages of artificial neural networks and neuro-fuzzy systems. This method is applied to the XOR problem, n bit parity problem, which is used as the benchmark in the field of pattern recognition. The method is also applied to the recognition of digital image for practical image application. As a result of experiment, it does not always guarantee convergence. However, the network showed considerable improvement in learning time and has a high convergence rate. The proposed network can be extended to any number of layers. When we consider only the case of the single layer, the networks had the capability of high speed during the learning process and rapid processing on huge images.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.176.4-176
• /
• 2001

In this paper, a novel type of iterative learning controller is studied. The proposed learning algorithm utilizes not only the error signal of the previous iteration but also the delayed error signal of the current iteration. The delayed error signal is adopted to improve the convergence speed. The convergence condition is examined and the result shows that the proposed learning algorithm shows the fast convergence speed under the same convergence condition of the traditional iterative learning algorithm. The simulation examples are presented to confirm the validity of the proposed ILC algorithm.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.40 no.5
• /
• pp.410-416
• /
• 2003

In the affine projection algorithm(APA), the projection dimension depends on a number of projection basis and of elements of input vector used for updating of coefficients of the adaptive filter. The projection dimension is closely related to a convergence speed of the APA, and it determines computational complexity. As the adaptive filter approaches to steady state, convergence speed is decreased. Therefore it is possible to reduce projection dimension that determines convergence speed. In this paper, we proposed the variable dimension affine projection algorithm (VDAPA) that controls the projection dimension and uses the relation between variations of coefficients of the adaptive filter and convergence speed of the APA. The proposed method reduces computational complexity of the APA by modifying the number of projection basis on convergence state. For demonstrating the good performances of the proposed method, simulation results are compared with the APA and normalized LMS algorithm in convergence speed and computational quantity.

• Journal of information and communication convergence engineering
• /
• v.8 no.3
• /
• pp.267-272
• /
• 2010

For the last decade, recurrent neural networks (RNNs) have been commonly applied to communications channel equalization. The major problems of gradient-based learning techniques, employed to train recurrent neural networks are slow convergence rates and long training sequences. In high-speed communications system, short training symbols and fast convergence speed are essentially required. In this paper, the derivative-free Kalman filter, so called the unscented Kalman filter (UKF), for training a fully connected RNN is presented in a state-space formulation of the system. The main features of the proposed recurrent neural equalizer are fast convergence speed and good performance using relatively short training symbols without the derivative computation. Through experiments of nonlinear channel equalization, the performance of the RNN with a derivative-free Kalman filter is evaluated.