• The Journal of the Acoustical Society of Korea
• /
• v.27 no.3E
• /
• pp.95-103
• /
• 2008

In this paper, the new hybrid adaptive Volterra filter was proposed to be applied for compensating the nonlinear distortion of memoryless nonlinear systems with saturation characteristics. Through computer simulations as well as the analytical analysis, it could be shown that it is possible for both conventional Volterra filter and proposed hybrid Volterra filter, to be applied for linearizing the memoryless nonlinear system with nonlinear distortion. Also, the simulations results demonstrated that the proposed hybrid filter may have faster convergence speed and better capability of compensating the nonlinear distortion than the conventional Volterra filter.

• Journal of IKEEE
• /
• v.17 no.2
• /
• pp.111-118
• /
• 2013

In this paper, the subband nonlinear adaptive Volterra filters are introduced and its analysis are presented. From the eigenvalue analysis of the input correlation matrix, we show that the proposed subband adaptive Volterra filter has superior convergence performance as compared to the conventional one, which shows that the it can be useful for the recently proposed subband nonlinear adaptive echo canceller. Also, the optimum filter in each subband are introduced and verified from the computer simulations.

• 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.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.16-19
• /
• 1996

The objective of this paper is to present a new adaptive nonlinear compensation method, which is based upon the Pth-order inverse theory and can be implemented in a systematic way, for weakly nonlinear systems that can be modeled by a Volterra series. In particular, employment of the proposed approach for the linearization of a given nonlinear system leads to the effective elimination of (up to a required order) nonlinearities in the overall system output. To demonstrate the feasibility of the proposed method, simulation results using a satellite communication system model are also provided.

• Journal of IKEEE
• /
• v.24 no.2
• /
• pp.570-578
• /
• 2020

In this paper, we propose an efficient method for implementing the adaptive second-order Volterra filter. To reduce computational load, the UCFD-SVF has been proposed. The UCFD-SVF, however, shows deteriorated convergence performance. We propose a new method that initializes the adaptive filter weights periodically on the fact that the energy of the filter weights is slowly increased. Furthermore, we propose another method that the interval for the weight initialization is variable to guarantee the performance and we shows the method gives the better performance under the non-stationary environment through the computer simulation for the adaptive system identification.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.98.5-98
• /
• 2001

An efficient adaptive nonlinear compensator, based on a wavelet transform domain adaptive Volterra filter along with a modified escalator algorithm, is proposed to speed up the convergence rate of an adaptive LMS algorithm. In particular, it is well known that the e.g., slow convergence speed of an adaptive LMS algorithm depends on the statistical characteristics (e.g., large eigenvalue spread) of the corresponding auto-correlation matrix of the input vector. To solve such a convergence problem, the proposed approach utilizes a modified escalator algorithm and a wavelet transform domain adaptive LMS Volterra filtering technique, which leads to diagonalization of the auto-correlation matrix of the ...

• Proceedings of the KIEE Conference
• /
• 1996.07b
• /
• pp.1289-1291
• /
• 1996

In this paper, we propose a new adaptive pre-processing technique for the linearization of a weakly nonlinear system which can be modeled by a Volterra series up to third order. To compensate the nonlinear effects of a given system, an update algorithm for the linear filter coefficients of the proposed adaptive pre-processor is introduced, and to compensate the linear distortion of the given system, the linear inverse filter is also utilized. For the performance test of the proposed adaptive pre-processor, computer simulation results obtained by analyzing an ANRSS loudspeaker model are provided.

• Proceedings of the KIEE Conference
• /
• 2008.04a
• /
• pp.238-239
• /
• 2008

In this paper, active noise control (ANC) of a Volterra system with a nonlinear secondary path is proposed in the presence of a linear acoustic feedback, whereby the conventional ANC of a linear system with online acoustic feedback-path modeling is further extended to ANC of a Volterra system with a linear acoustic feedback path. In particular, the proposed ANC system consists of two adaptive Volterra filters (for nonlinear noise control and nonlinear adaptive noise cancellation) and one feedback-path modeling filter. Simulation results show that the proposed approach yields more effective reduction of disturbances arising from the acoustic feedback, in addition to high nonlinear ANC performance.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.22 no.4
• /
• pp.696-705
• /
• 1997

This paper presents an efficient adaptive predistortion technique compensating linear and nonlinear distortions caused by high-power amplifier (HPA) with memory in OFDM systems. The efficient adaptive data predistortion techniques proposed for compensation of HPA with memory in single carrier systems cannot be applied to OFDM systems since the possible input levels for HPA is infinite in OFDM systems. Also, previous adaptive predistortion techniques, based on Volterra series modeling, are not suitable for real-time implementation due to high computational burden and slow convergence rate. In the proposed approach, the memoryless HPA preceded by a linear filter in OFDM systems is modeled by the Wiener system which is then precompensated by the proposed adaptive predistorter with a minimum number of filter taps. An adaptive algorithm for adjusting the proposed adaptive predistorter is derived using the stochastic gradient method. It is demonstrated by computer simulation that the performance of OFDM system suffering from nonlinear distortion can be greatly improved by the proposed efficient adaptive predistorter using a small number of filter taps.

• Proceedings of the KOSOMBE Conference
• /
• v.1995 no.11
• /
• pp.282-285
• /
• 1995

In this paper, we present a feedback structured adaptive RF filter based on the recursive modified Gram-Schmidt algorithm for short-term prediction of EEG signal. And the performance of this proposed filter is compared with those of linear AR model, RF filter, Volterra filter and RBF neural network as single-step prediction and multi-step prediction. The results show the superiority of this proposed filter in prediction of EEG signals.