摘要
通过对轨道车辆车内含噪样本数据的分析,应用步长因子μ(n)与误差信号e(n)呈正弦函数关系的变步长LMS算法。分别对自适应滤波器中的权向量按照最速下降算法进行更新,并利用建立的自适应滤波器进行车内噪声主动控制。结果表明,提出的变步长LMS算法解决了LMS算法因固定步长不能同时兼顾算法收敛速度和稳态误差的固有缺陷,具有更快的算法收敛速度和较小的稳态误差。
By analyzing the noise signal sample inside the railway vehicle, the plain LMS algorithm and the LMS algorithmwith variable-incremental-steps were applied respectively to update the weight vectors in the adaptive filtering basedon the steepest descent algorithm. The relation between step factor μ（n） and error signal e（n） is a sinusoidal function in thevariable-step LMS algorithm. The adaptive filter was used for active internal noise control for the vehicle. Result shows thatthe proposed variable-step LMS algorithm can overcome the inherent contradiction in the plain LMS algorithm between algorithmconvergence speed and steady-state error, and has faster algorithm convergence speed and less steady-state error simultaneously.
出处
《噪声与振动控制》
CSCD
2015年第1期123-126,共4页
Noise and Vibration Control
基金
国家自然科学基金项目(51175320)
上海市自然科学基金项目(14ZR1418600)
关键词
声学
主动控制
变步长LMS算法
车内噪声
acoustics
active control
variable-incremental-step LMS algorithm
vehicle interior noise
