摘要
反馈有源噪声控制系统结构简单,抗外界干扰能力强,但在对频率成分复杂的噪声控制时通常存在收敛速度慢、控制残差高等缺陷。本文针对工程中常见的离散线谱噪声反馈控制问题设计了一种最近邻-多频陷波器(NNR-MNF)反馈有源控制模型,使用最近邻回归器算法先验计算时域最优滤波器系数的近似解,在参数空间内从一个接近最优解的位置开始训练滤波器参数,使系统能够以一个较小的步长对复杂噪声进行控制,在快速收敛的同时避免系统发散问题。基于驱逐舰轮机噪声真实数据的计算机仿真实验结果表明,NNR-MNF算法相比传统控制方法其收敛时间减少了约70%。该结果说明使用基于机器学习的参数预训练方法能够有效提升噪声有源控制系统的收敛速度,为主动噪声控制问题提供了一种新的优化方案。
Noise feedback active control systems usually have the disadvantage of slow convergence and high control residuals.This paper has designed a Nearest Neighbors-Multifrequency Notch-Filter(NNR-MNF)model for dealing with line spectral noise.It calculates the approximate solution of the optimal filter coefficients in the time domain and then iterates the filter parameters from a position close to the optimal solution.This procedure allows the system to control complex noise by using a small learning rate.As a result,it can reach rapid convergence while avoiding the problem of system divergence.The experiment results based on the destroyer engine noise dataset show that the NNR-MNF algorithm reduces the convergence time by about 70%compared with the traditional control method.The results show that the use of neural network methods based on machine learning can effectively improve the control speed of noise active control systems,and provide a new optimization solution for active noise control problems.
作者
闫宏生
施麟
李佳勇
闫一天
唐俊
Yan Hongsheng;Shi Lin;Li Jiayong;Yan Yitian;Tang Jun(Schoolof Civil Engineering,Tianjin University,Tianjin 300072,China;Tianjin Key Laboratory of Port and Ocean Engineering,Tianjin University,Tianjin 300072,China)
出处
《电子测量技术》
北大核心
2022年第22期47-54,共8页
Electronic Measurement Technology
基金
装备预研领域基金(61402100104)项目资助。
关键词
有源噪声控制
机器学习
最近邻回归
active noise control
machine learning
nearest neighbors regressor
