Active noise control by using prediction of time series data with a neural network

Matsuura, T.; Hiei, Takehiko; Itoh, Hiroyuki; Torikoshi, Kunikazu

doi:10.1109/icsmc.1995.538084

Cited by 27 publications

(12 citation statements)

References 2 publications

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“…[4][5][6][7][8][9][10] The noise produced by a dynamic system may, however, be a nonlinear and deterministic noise process rather than a stochastic, white, or tonal noise process. [11][12][13][14][15][16][17][18] In this case, the traditional linear ANC algorithm cannot be used to control the noise. Additionally, the secondary path often shows a non-minimum phase response.…”

Section: Introductionmentioning

confidence: 99%

“…A number of nonlinear ANC algorithms have been proposed in the past and have shown better performance over the FXLMS algorithm for chaotic input signals and secondary paths with non-minimum phase response. [11][12][13][14][15][16][17][18][19][20][21][22][23][24] Of the many nonlinear ANC algorithms, artificial neural network, 13,14 radial basis function based neural network, 11 Volterra series based algorithms, 15,16,19 and functional link neural network (FLANN) based filtered-s least mean square algorithms (FSLMS) 18,[20][21][22][23][24] are the most popular. The FSLMS algorithm has the advantage of low computational complexity and better performance over the Volterra filteredx least mean square (VFXLMS) algorithm.…”

Section: Introductionmentioning

confidence: 99%

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A nonlinear active noise control algorithm for virtual microphones controlling chaotic noise

Das

Moreau

Cazzolato

2012

The Journal of the Acoustical Society of America

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In active noise control (ANC) systems, virtual microphones provide a means of projecting the zone of quiet away from the physical microphone to a remote location. To date, linear ANC algorithms, such as the filtered-x least mean square (FXLMS) algorithm, have been used with virtual sensing techniques. In this paper, a nonlinear ANC algorithm is developed for a virtual microphone by integrating the remote microphone technique with the filtered-s least mean square (FSLMS) algorithm. The proposed algorithm is evaluated experimentally in the cancellation of chaotic noise in a one-dimensional duct. The secondary paths evaluated experimentally exhibit non-minimum phase response and hence poor performance is obtained with the conventional FXLMS algorithm compared to the proposed FSLMS based algorithm. This is because the latter is capable of predicting the chaotic signal found in many physical processes responsible for noise. In addition, the proposed algorithm is shown to outperform the FXLMS based remote microphone technique under the causality constraint (when the propagation delay of the secondary path is greater than the primary path). A number of experimental results are presented in this paper to compare the performance of the FSLMS algorithm based virtual ANC algorithm with the FXLMS based virtual ANC algorithm.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A nonlinear active noise control algorithm for virtual microphones controlling chaotic noise

Das

Moreau

Cazzolato

2012

The Journal of the Acoustical Society of America

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“…Several sources of nonlinearity may affect the described system, ranging from the noise characteristics [5] to the dynamics of the involved acoustic paths [6]. Distortion and saturation of microphones, amplifiers, loudspeakers and converters are commonly experienced in practice [7].…”

Section: Introductionmentioning

confidence: 99%

A dual filtering scheme for nonlinear active noise control

Delvecchio

Piroddi

2013

Adaptive Control & Signal

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Active noise control problems are often affected by nonlinear effects such as distortion and saturation of measurement and actuation devices, which call for suitable nonlinear models and algorithms. The active noise control problem can be interpreted as an indirect model identification problem, due to the secondary path dynamics that follow the control filter block. This complicates the weight update mechanism in the nonlinear case, in that the error gradient depends on the secondary path gradient through nonlinear recursions. A simpler and computationally less demanding approach is here proposed that employs the updating scheme of the standard filtered-x least mean squares (LMS) or filtered-u LMS algorithm. As in those schemes, the calculation of the error gradient requires a signal filtering through an auxiliary system, here obtained through a secondary adaptation loop. The resulting dual filtering LMS algorithm performs the adaptation of the controller parameters in a direct identification mode and can therefore be easily coupled with adaptive model structure selection schemes to provide online tuning of the model structure, for improved model robustness.

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“…Research has shown that the noise measured from a ventilation fan exhibit chaotic behaviour [28]. Three kinds of chaotic noises in the form of Logistic, Lorenz, and Duffing noise filters have been applied to test the capability of a proposed single channel nonlinear controller [29].…”

Section: Reference Noisementioning

confidence: 99%

Comparison of Performance and Computational Complexity of Nonlinear Active Noise Control Algorithms

Sahib

Kamil

2011

ISRN Mechanical Engineering

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Research on nonlinear active noise control (NANC) revolves around the investigation of the sources of nonlinearity as well as the performance and computational load of the nonlinear algorithms. The nonlinear sources could originate from the noise process, primary and secondary propagation paths, and actuators consisting of loudspeaker, microphone or amplifier. Several NANCs including Volterra filtered-x least mean square (VFXLMS), bilinear filtered-x least mean square (BFXLMS), and filtereds least mean square (FSLMS) have been utilized to overcome these nonlinearities effects. However, the relative performance and computational complexities of these algorithm in comparison to FXLMS algorithm have not been carefully studied. In this paper, systematic comparisons of the FXLMS against the nonlinear algorithms are evaluated in overcoming various nonlinearity sources. The evaluation of the algorithms performance is standardized in terms of the normalized mean square error while the computational complexity is calculated based on the number of multiplications and additions in a single iteration. Computer simulations show that the performance of the FXLMS is more than 80% of the most effective nonlinear algorithm for each type of nonlinearity sources at the fraction of computational load. The simulation results also suggest that it is more advantageous to use FXLMS for practical implementation of NANC.

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Active noise control by using prediction of time series data with a neural network

Cited by 27 publications

References 2 publications

A nonlinear active noise control algorithm for virtual microphones controlling chaotic noise

A nonlinear active noise control algorithm for virtual microphones controlling chaotic noise

A dual filtering scheme for nonlinear active noise control

Comparison of Performance and Computational Complexity of Nonlinear Active Noise Control Algorithms

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