Multi-reference Adaptive Gain FXLMS Algorithm for Active Noise Control

Quanjiang Wu, Pengwei Wen, X. Chai, Hui Yang, Jiaxin Chen, Limin Zhang
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引用次数: 0

Abstract

Multi-reference least mean square algorithm (MR-FXLMS) has been performed more efficiently than the traditional Least mean square algorithm (FXLMS). In this paper, a multi-reference adaptive gain (MRAG-FXLMS) algorithm which is designed to enhance the performance of the multi-reference FXLMS algorithm. The proposed method is derived by combining a novel multi-reference structure and adaptive gain. The adaptive gain is obtained by using the gradient descent method. To evaluate the effectiveness of the proposed algorithm, a computational complexity analysis is conducted. According to the simulation results, it can be concluded that the proposed MRAG-FXLMS algorithm achieves excellent performance in convergence rate and steady-state error compared with the conventional FXLMS algorithm and MR-FXLMS algorithm.1
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多参考自适应增益FXLMS算法的主动噪声控制
多参考最小均方算法(MR-FXLMS)比传统的最小均方算法(FXLMS)更有效。本文提出了一种多参考自适应增益(MRAG-FXLMS)算法,旨在提高多参考自适应增益算法的性能。该方法结合了一种新的多参考结构和自适应增益。采用梯度下降法获得自适应增益。为了评估所提算法的有效性,进行了计算复杂度分析。仿真结果表明,与传统的FXLMS算法和MR-FXLMS算法相比,所提出的MRAG-FXLMS算法在收敛速度和稳态误差方面都具有优异的性能
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