Lattice form adaptive infinite impulse response filtering algorithm for active noise control

An active noise control algorithm is introduced based on adaptive wavelet networks using rationale functions with second-order poles wavelets. A novel network structure is derived using a nonlinear static mapping cascaded with an infinite impulse response filter wherein only input at the current step is needed to generate the output of the next sample. Therefore, incorporating tap delayed line of input–output of the physical system can be eliminated, avoiding the use of multidimensional wavelet networks. Online dynamic back-propagation learning algorithms (based on gradient descent method) are applied to adjust the network parameters. The local convergence of the closed-loop system is proved using discrete Lyapunov function. Simulations are carried out to compare the performance of proposed methods with other nonlinear algorithms (e.g. FxBPNN, Volterra, FLNN, and RFFLNN). Experiments are then conducted to evaluate the developed algorithms. Both simulation and experimental results show the superior performance of proposed method in terms of fast convergence rate and noise attenuation while avoiding curse of dimensionality.

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Feedforward active noise control using wavelet frames: simulation and experimental results

Journal of Vibration and Control ◽

10.1177/1077546315581939 ◽

2016 ◽

Vol 23 (4) ◽

pp. 555-573 ◽

Cited By ~ 4

Author(s):

Mahdi Akraminia ◽

Mohammad J Mahjoob ◽

Amir H Niazi

Keyword(s):

Noise Control ◽

Active Noise Control ◽

Back Propagation ◽

Experimental Results ◽

Superior Performance ◽

Infinite Impulse Response ◽

Gradient Descent Method ◽

Least Mean Square ◽

Wavelet Frames ◽

Active Noise

Wavelet frames are an appropriate alternative approach for signal approximation especially for noise/vibration signals. This paper presents an active noise control scheme using various wavelet frames with an innovative structure. In this structure, the authors employ a nonlinear static mapping cascaded with an infinite impulse response filter to model the dynamic part of the network. Online dynamic back propagation learning algorithms are applied based on the gradient descent method to adjust the network parameters. Simulations are carried out to compare the performance of filtered-x least mean square and filtered-x back propagation neural network algorithms with the proposed method. Experiments are then designed and conducted to evaluate the developed algorithms. Both simulation and experimental results show the superior performance of the proposed method in terms of a fast convergence rate and noise attenuation while avoiding the curse of dimensionality.

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Length Variation Effect of the Impulse Response Model of a Secondary Path in Embedded Control

Journal of Sensors ◽

10.1155/2016/8270121 ◽

2016 ◽

Vol 2016 ◽

pp. 1-7 ◽

Cited By ~ 2

Author(s):

Young-Sup Lee ◽

Yunseon Choi ◽

Jeakwan Kim

Keyword(s):

Impulse Response ◽

Noise Control ◽

Active Noise Control ◽

Path Model ◽

Length Variation ◽

Control Board ◽

Computation Complexity ◽

Embedded Control ◽

Active Noise ◽

Fxlms Algorithm

This study presents theoretical and experimental investigation on the length variation effect of the impulse response function (IRF) for the secondary path model in active noise control using an embedded control board. A narrowband sweep noise was the disturbance for control in a duct with the length of 1800 mm. The IRF model incorporated into an adaptive feedforward filtered-x LMS (FxLMS) algorithm was then analyzed in the variation of its length in terms of the mean square error, computation complexity, stability requirement, and attenuation performance before and after control. The FxLMS algorithm with various IRF lengths was implemented in a dSPACE DS1104 embedded control board for the real-time control. Finally the most reasonable IRF length, considering the computation complexity and performance, can be determined through the systematic investigation. The results in this study can be used for practical active noise control systems.

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