• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.16 no.5
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• pp.112-118
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• 2002

The adaptive m filters is more effective than m filters when acoustic feedback exists, in which cause an order of a FIR filter must be very large if some of poles of the ideal control filter are near the unit circle. But the IIR filters may have stability problems especially when the adaptive algorithm is not converged. In this paper, a stabilizing procedure for adaptive IIR filters is proposed. In the beginning of the ANC system, it improve a stability by pulling the poles of the IIR filter to the center of the unit circle, and it returns the poles to their original positions after the filter converge. Computer simulations and experiments are performed to show the effectiveness of proposed schemes.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3118-3120
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• 2000

In this paper. a stable IIR adaptive filter for active noise control is proposed. The IIR filter structure is more effective when acoustic feedback exists, in which case an order of a FIR filter must be very large if some of the poles of the ideal control filter are near the unit circle. But the IIR filter may have stability problems especially when the adaptive algorithm is not converged. A stabilizing procedure for IIR adaptive filter is presented in this paper, and computer simulation is performed to show the effectiveness of proposed schemes.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.4
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• pp.370-378
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• 2002

Fuzzy inference which combines numerical data and linguistic data has been used to design adaptive filter algorithms. In adaptive IIR filter design, the fuzzy prefilter is taken account, and applied to both direct and lattice structure. As for the fuzzy inference of the fuzzy filter, the Sugeno's method is employed. As membership functions and inference rules are recursively generated through neural network, the accuracy can be improved. The proposed adaptive algorithm, adaptive IIR filter with fuzzy prefilter, has been applied to adaptive system identification for the purposed of performance test. The evaluations have been carried out with viewpoints of convergence property and tracking properties of the parameter estimation. As a result, the faster convergence and the better coefficients tracking performance than those of the conventional algorithm are shown in case of direct structures.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.1
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• pp.150-155
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• 2011

In an active noise control systems, an IIR filter may cause a problem in stability beacause of its poles. For IIR filter, its poles goes sometimes out of a unit circle in a z-plane in the transition state, where the adaptive algorithm converges to the optimum value, which causes the system to diverge. Fuzzy LMS algorithm has a better convergence property than conventional LMS algorithms, but is not applicable to IIR filter because of the reasons. Stabilized adaptive algorithm could be improves stability by moving the pole of IIR filer toward the origin forcibly in the transient state, and by introducing forgetting factor to maintain the optimum convergence when it reaches to the steady state. In this paper, We proposed stabilized adaptive fuzzy LMS algorithms with IIR filter structures, for single channel active noise control with ill conditioned signal case. Computer simulations were performed to show the effectiveness of a proposed algorithm.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.8
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• pp.375-377
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• 2006

An adaptive IIR filter in ANC(Active Noise Control) systems is more effective than an adaptive FIR filter when acoustic feedback exists, in which cause an order of an adaptive FIR filter must be very large if some of poles of the ideal control filter are near the unit circle. But the IIR filters may have stability problems especially when the adaptive algorithm for adaptive filters is not yet converged. In this paper, a stabilized multi-channel recursive LMS (MCRLMS) algorithm for an adaptive multi-channel IIR filter is presented. RLMS algorithms usually diverge before the algorithm is not yet converged. So, in the beginning of the ANC system, the stability of the RLMS algorithms could be improved by pulling the poles of the IIR filter to the center of the unit circle, and returning the poles to their original positions after the filter converges. Computer simulations and experiments for dipole ducts using a TMS320C32 digital signal processor have performed to show the effectiveness of a proposed algorithm.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.30-32
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• 2006

An adaptive IIR filter in ANC(Active Noise Control) systems is more effective than an adaptive FIR filter when acoustic feedback exists, in which cause an order of an adaptive FIR filter must be very large if some of poles of the ideal control filter are near the unit circle. But the IIR filters may have stability problems especially when the adaptive algorithm for adaptive filters is not yet converged. In this paper, a stabilized multi-channel recursive LMS (MCRLMS) algorithm for an adaptive multi-channel IIR filter is presented. RLMS algorithms usually diverge before the algorithm is not yet converged. So, in the beginning of the ANC system, the stability of the RLMS algorithms could be Improved by pulling the poles of the IIR filter to the center of the unit circle, and returning the poles to their original positions after the filter converges. Computer simulations and experiments for dipole ducts using a TMS320C32 digital signal processor have performed to show the effectiveness of a proposed algorithm.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.3066-3068
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• 2005

Noise can make surrounding environments inferior and deteriorates operation efficiency, and it can bring aural damage as well as give a person psychological stress. Therefore, necessity of study about noise control is increased for better labor conditions and agreeable habitat. In this paper, implementation of active mufflers using a stable IIR adaptive filters is presented. The IIR filter structure is more effective when acoustic feedback exists, but the adaptive IIR filters could be unstable when the filter algorithm is not yet converged. A stabilizing process for adaptive IIR filter is introduced in this paper. Experiments using a TMS320C32 digital signal processor have performed to show the effectiveness of a proposed algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3647-3653
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• 2012

In active noise control filter, IIR filter structure which used for control filter assures the stability property. The stability characteristics of IIR filter structure is mainly determined by pole location of control filter within unit disc, so stable selection of the value of control filter coefficient is very important. In this paper, we proposed novel adaptive stabilized Filtered_U LMS algorithms with IIR filter structure which has better convergence speed and less computational burden than conventional FIR structures, for multi-channel active noise control with vehicle enclosure signal case. For better convergence speed in adaptive algorithms, fuzzy LMS algorithms where convergence coefficient computed by a fuzzy PI type controller was proposed.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2555-2557
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• 2001

The adaptive IIR filters is more effective than adaptive FIR filters which have the same order of IIR filters. But the IIR filters may have stability problems especially when the adaptive algorithm is not converged. In this paper, a stabilizing procedure for adaptive IIR filters is proposed, and computer simulation is performed to show the effectiveness of proposed schemes.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.629-631
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• 1998

An adaptive joint-process IIR filter with generalized lattice structure is implemented by modifying the conventional lattice filter and making an adaptive algorithm in a system identification problem.