• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.941-946
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• 2005

In this paper an active random noise control using adaptive learning rate neural networks is presented. The adaptive learning rate strategy increases the learning rate by a small constant if the current partial derivative of the objective function with respect to the weight and the exponential average of the previous derivatives have the same sign, otherwise the learning rate is decreased by a proportion of its value. The use of an adaptive learning rate attempts to keep the learning step size as large as possible without leading to oscillation. It is expected that a cost function minimize rapidly and training time is decreased. Numerical simulations and experiments of active random noise control with the transfer function of the error path will be performed, to validate the convergence properties of the adaptive learning rate Neural Networks. Control results show that adaptive learning rate Neural Networks control structure can outperform linear controllers and conventional neural network controller for the active random noise control.

• Transactions of the Korean Society of Automotive Engineers
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• v.2 no.6
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• pp.29-37
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• 1994

Active noise control of random noise which propatate in the vehicle cabin as a form of spherical wave is the target of this study. In the previous study, the adaptive algorithm for adaptive controller is presented for the application in active noise control system. And for the preliminary study of adaptive active noise control in vehicle cabin as a real system, a computer simulation is performed on the effectiveness of the adaptive algorithm in the amplitude of the pressure fluctuation. This work studies the implementation of multi-channel feedforward adaptive algorithm for the reduction of the noise inside a vehicle cabin using a number of secondary sources derived by adaptive filtering of reference noise source. Multi-channel adaptive feedforward algorithm are verified in numerical simulation and semi-experimental justification of developed system is made on a domestic passenger car. In the results of semi-experimental study, the noise of specific region in the interior of automobile are reduced for the appreciabe sound pressure level in the operating engine rpm and finally this study suggests the capabilities of the real time active noise control in 3 dimensional acoustic fields.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.8
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• pp.1332-1338
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• 1997

In the case of the transfer function for the secondary path is dependent on time, the on-line method which can model it is continuously must be applied to the active noise control technique. And the adaptive random noise technique among the on-line methods is effective in the narrow-band control. In this method, the signal to noise ratio between random noise for modeling and primary noise is low. Therefore, the estimations of transfer function will be prone to inaccuracies and the convergence time will be too long. Such imperfections will have an influence upon the performance of an active noise controller. In this study, t enhance the signal to noise ratio, the on-line method that is combined the conventional adaptive random noise technique and the adaptive line enhancer, is proposed. By using proposed on-line method, a rigorous system identification and control of primary noise have been implemented.

• Journal of KSNVE
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• v.10 no.6
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• pp.941-948
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• 2000

Active noise control is an approach to reduce the noise by utilizing a secondary noise source that destructively interferes with the unwanted noise. In general, active noise control systems rely on multiple sensors to measure the unwanted noise field and the effect of the cancellation. This paper develops an approach that utilizes a single sensor. The noise field is modeled as a stochastic process, and an adaptive algorithm is used to adaptively estimate the parameters of the process. Based on these parameter estimates, a canceling signal is generated. Oppenheim assumed that transfer function characteristics from the canceling source to the error sensor is only a propagation delay. This paper proposes a modified Oppenheim algorithm by considering transfer characteristics of speaker-path-sensor This transfer characteristics is adaptively cancelled by the proposed adaptive modeling technique. Feasibility of the proposed method is proved by computer simulations with artificially generated random noises and sine wave noise. The details of the proposed architecture. and theoretical simulation of the noise cancellation system for three dimension enclosure are presented in the Paper.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.13 no.4
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• pp.239-246
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• 2003

Application of the filtered-x Least Mean Square(LMS) adaptive filter to active noise control requires to estimate the transfer characteristics between the output and the error signal of the adaptive controller. In this paper, we derive the filtered-x adaptive noise control algorithm and analyze its convergence behavior when the acoustic noise consists of multiple sinusoids. The results of the convergence analysis of the filtered-x LMS algorithm indicate that the effects of the parameter estimation inaccuracy on the convergence behavior of the algorithm are characterized by two distinct components Phase estimation error and estimated gain. In particular, the convergence is shown to be strongly affected by the accuracy of the phase response estimate. Simulation results are presented to support the theoretical convergence analysis.

• The Journal of the Acoustical Society of Korea
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• v.34 no.1
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• pp.46-51
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• 2015

In fields of controlling acoustical noises, the overall adaptive control system is nonlinear due to the loudspeaker, amplifiers, converters, and microphones, etc. and the performance of noise control is decreased by the extent of nonlinearities, so an adaptive control system compensating nonlinear distortions is needed. In this paper, a new multi-channel adaptive noise controller was proposed, which was combined with the adaptive compensator to effectively linearize nonlinear distortions in the overall adaptive control system. Through computer simulations, the proposed adaptive compensator could linearize the nonlinear distortions and the proposed noise controller had better capability of controlling the noises than the conventional LMS controller.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1360-1363
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• 1997

Development and realizatioin of adaptive Active Noise Cntrol used for quieting transformer nosie are planed to provide workers with comfortable working environment and to attenuate the noise for residents in many noisy areas(power plant, power transformer, GIS transformer etc.).

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.50 no.3
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• pp.130-137
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• 2001

In this paper, we propose a new Active Noise Filter Control System which operate as a control performance when a adaptive filter fault. In this system, half-fixed filter which is new filter, connected to parallel with adaptive filter. An adaptive filler use to continuous parameter estimating, but adaptive filter is fault, half-fixed filter update newly data which is continuous estimating date each during sampling period. We simulate and apply the proposed active noise filter system to in the cylinder type duct. Experimental results show that proposed Active Noise Filter Control System has better control performance than existing filter which Eriksson's or Parallel Filter System in term of noise reduction.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1993.04a
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• pp.140-146
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• 1993

Active noise control uses the intentional superposition of acoustic waves to create a destructive interference pattern such that a reduction of the unwanted sound occurs. In active noise control system the choice of a control structure and design of the controller are the main issues of concern. In real acoustic fields there are a vast number of noise sources with time-varying nature and the characteristics of transducers and the geometric set-up of control system are subject to change. Accordingly the control system should be designed to adapt such circumstances so that required level of performance is maintained. In this paper, the adaptive control algorithm for self-tuning adaptive controller is presented for the application in active noise control system. Self-tuning is a direct integration of identification and controller design algorithm in such a manner that the two processes proceed sequentially. The least mean square algorithm was used for the identification schemes and adaptive weighted minimum variance control algorithm was applied for self-tuning controller. Computer simulation results for self-tuning feedback controller are presented. And simulation results was shown to be useful for the situation in which the periodic noise sources act on the acoustic field.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.8
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• pp.442-450
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• 2000

Active noise control is an approach to noise reduction in which a secondary noise source that destructively interferes with the unwanted noise. In general, active noise control systems rely on multiple sensors to measure the unwanted noise field and the effect of the cancellation. This paper develops an approach that utilizes a single sensor. The noise field is modeled as a stochastic process, and a time-adaptive algorithm is used to adaptively estimate the parameters of the process. Based on these parameter estimates, a canceling signal is generated. Opppenheim model assumed that transfer function characteristics from the canceling source to the error sensor is only propagation delay. But this paper proposes a modified Oppenheim model by considering transfer characteristics of acoustic device and noise path. This transfer characteristics is adaptively cancelled by adaptive model. This is proved by computer simulation with artifically generated random noise and sine wave noise. The details of the proposed architecture, and theoretical simulation and experimental results of the noise cancellation system for three dimension enclosure are presented in the paper.