A bilinear functional link artificial neural network filter for nonlinear active noise control and its stability condition

被引：30
作者：
Le, Dinh Cong [1 ]
Zhang, Jiashu [1 ]
Pang, Yanjie [1 ]
机构：
[1] Southwest Jiaotong Univ, Sichuan Prov Key Lab Signal & Informat Proc, Chengdu 610031, Sichuan, Peoples R China
来源：
APPLIED ACOUSTICS | 2018年 / 132卷
基金：
美国国家科学基金会;
关键词：
Nonlinear active noise control; FLANN; Generalized FLANN; Bilinear filter; S LMS ALGORITHM; CONTROL SYSTEM; CHAOTIC NOISE; MICROPHONES; MITIGATION; VIBRATION; HEADREST;
D O I：
10.1016/j.apacoust.2017.10.023
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
Since the functional link artificial neural network (FLANN) filter using trigonometric expansions do not exploit cross-terms (products of input samples and /or past output samples with different time shifts), its performance for nonlinear active noise control (ANC) can be considerably degraded, especially in strong nonlinearity environment. In order to overcome this drawback, a novel bilinear FLANN (BFLANN) filter for the nonlinear ANC is proposed in this paper. In addition, a sufficient condition that guarantees the stability of the BFLANN filter is also presented. Simulation results demonstrate that the proposed BFLANN filter based nonlinear ANC can achieve better performance than the FLANN and generalized FLANN (GFLANN) filters based nonlinear ANC in the presence of strong nonlinearity.
引用
收藏
页码：19 / 25
页数：7
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