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A novel adaptive active noise control algorithm based on Tikhonov regularisation
Author(s): Iman Ardekani; Neda Sakhaee; Hamid Sharifzadeh; Bashar Barmada; Gerard Lovell
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Paper Abstract

This paper proposes a novel adaptive active noise control algorithm based on Tikhonov regularization theory. A regularized cost function consisting of the weighted sum of the most recent samples of the residual noise and its derivative is defined. By setting the gradient vector of the cost function to zero, an optimal solution for the control parameters is obtained. Based on the proposed optimal solution, a computationally efficient algorithm for adaptive adjustment of the control parameters is developed. It is shown that the regularized affine projection algorithm can be considered as a very special case of the proposed algorithm. Different computer simulation experiments show the validity and efficiency of the proposed algorithm.

Paper Details

Date Published: 17 April 2019
PDF: 5 pages
Proc. SPIE 11071, Tenth International Conference on Signal Processing Systems, 1107109 (17 April 2019); doi: 10.1117/12.2520450
Show Author Affiliations
Iman Ardekani, Unitec Institute of Technology (New Zealand)
Neda Sakhaee, The Univ. of Auckland (New Zealand)
Hamid Sharifzadeh, Unitec Institute of Technology (New Zealand)
Bashar Barmada, Unitec Institute of Technology (New Zealand)
Gerard Lovell, Unitec Institute of Technology (New Zealand)


Published in SPIE Proceedings Vol. 11071:
Tenth International Conference on Signal Processing Systems
Kezhi Mao; Xudong Jiang, Editor(s)

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