Nonlinear active noise control using Lyapunov theory and RBF network

Seng Kah Phooi; Man Zhihong; H. R. Wu

Nonlinear active noise control using Lyapunov theory and RBF network

Seng Kah Phooi^*, Man Zhihong, H. R. Wu

^*Corresponding author for this work

University of Tasmania

Research output: Contribution to conference › Paper › peer-review

4 Citations (Scopus)

Abstract

A new approach in design an efficient algorithm for ANC system is proposed. The transversal filter-based controllers (FIR and IIR) are first considered. A Lyapunov function of the error is first defined and filter coefficients are then adaptively adjusted based on Lyapunov stability theory so that the error converges to zero asymptotically. The design is independent of the statistic properties of signals and its computation complexity is comparable to FXLMS. It has fast error convergence properties and the stability is guaranteed by Lyapunov stability theory. This scheme can be further extended to an efficient nonlinear ANC using RBF network for excellent performance. Simulation examples are demonstrated to show the degree of noise cancellation this scheme can achieve.

Original language	English
Pages	916-925
Number of pages	10
Publication status	Published - 2000
Externally published	Yes
Event	10th IEEE Workshop on Neural Network for Signal Processing (NNSP2000) - Sydney, Australia Duration: 11 Dec 2000 → 13 Dec 2000

Conference

Conference	10th IEEE Workshop on Neural Network for Signal Processing (NNSP2000)
City	Sydney, Australia
Period	11/12/00 → 13/12/00

Cite this

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title = "Nonlinear active noise control using Lyapunov theory and RBF network",

abstract = "A new approach in design an efficient algorithm for ANC system is proposed. The transversal filter-based controllers (FIR and IIR) are first considered. A Lyapunov function of the error is first defined and filter coefficients are then adaptively adjusted based on Lyapunov stability theory so that the error converges to zero asymptotically. The design is independent of the statistic properties of signals and its computation complexity is comparable to FXLMS. It has fast error convergence properties and the stability is guaranteed by Lyapunov stability theory. This scheme can be further extended to an efficient nonlinear ANC using RBF network for excellent performance. Simulation examples are demonstrated to show the degree of noise cancellation this scheme can achieve.",

author = "Phooi, {Seng Kah} and Man Zhihong and Wu, {H. R.}",

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AU - Phooi, Seng Kah

AU - Zhihong, Man

AU - Wu, H. R.

PY - 2000

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N2 - A new approach in design an efficient algorithm for ANC system is proposed. The transversal filter-based controllers (FIR and IIR) are first considered. A Lyapunov function of the error is first defined and filter coefficients are then adaptively adjusted based on Lyapunov stability theory so that the error converges to zero asymptotically. The design is independent of the statistic properties of signals and its computation complexity is comparable to FXLMS. It has fast error convergence properties and the stability is guaranteed by Lyapunov stability theory. This scheme can be further extended to an efficient nonlinear ANC using RBF network for excellent performance. Simulation examples are demonstrated to show the degree of noise cancellation this scheme can achieve.

AB - A new approach in design an efficient algorithm for ANC system is proposed. The transversal filter-based controllers (FIR and IIR) are first considered. A Lyapunov function of the error is first defined and filter coefficients are then adaptively adjusted based on Lyapunov stability theory so that the error converges to zero asymptotically. The design is independent of the statistic properties of signals and its computation complexity is comparable to FXLMS. It has fast error convergence properties and the stability is guaranteed by Lyapunov stability theory. This scheme can be further extended to an efficient nonlinear ANC using RBF network for excellent performance. Simulation examples are demonstrated to show the degree of noise cancellation this scheme can achieve.

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M3 - Paper

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T2 - 10th IEEE Workshop on Neural Network for Signal Processing (NNSP2000)

Y2 - 11 December 2000 through 13 December 2000

ER -

Nonlinear active noise control using Lyapunov theory and RBF network

Abstract

Conference

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