针对线性窄带主动噪声控制(Linear narrowband active noise control,NANC)抑制变压器非线性噪声性能不佳的问题,提出一种基于Volterra和FIR组合滤波的变压器噪声非线性主动控制方法(Volterra and FIR filter-based nonlinear active no...针对线性窄带主动噪声控制(Linear narrowband active noise control,NANC)抑制变压器非线性噪声性能不佳的问题,提出一种基于Volterra和FIR组合滤波的变压器噪声非线性主动控制方法(Volterra and FIR filter-based nonlinear active noise control,VFNLANC)。该方法通过Volterra滤波器的谐波抑制特性处理变压器的非线性噪声,并将通过Volterra滤波器的滤波信号作为FIR滤波器的输入进行二次滤波,以缩小Volterra滤波器的截断误差。考虑变压器声道具有时变特性且存在扰动噪声,在VFNLANC方法中设计次级通道混合辨识架构,通过离线辨识生成初始通道参数并结合在线辨识实时更新参数,可既保证辨识精度又降低计算复杂度。采用实际变压器噪声数据对所提方法进行了仿真实验,结果显示VFNLANC方法的降噪量比NANC方法提升3.2~6 dB。展开更多
In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained f...In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained from the Euler-Maruyama discretization of the underlying stochastic differential equations(SDEs),based on which the loss function is built.The stochastic gradient descent method is applied in the neural network training.Numerical experiments demonstrate the effectiveness of our method.展开更多
文摘针对线性窄带主动噪声控制(Linear narrowband active noise control,NANC)抑制变压器非线性噪声性能不佳的问题,提出一种基于Volterra和FIR组合滤波的变压器噪声非线性主动控制方法(Volterra and FIR filter-based nonlinear active noise control,VFNLANC)。该方法通过Volterra滤波器的谐波抑制特性处理变压器的非线性噪声,并将通过Volterra滤波器的滤波信号作为FIR滤波器的输入进行二次滤波,以缩小Volterra滤波器的截断误差。考虑变压器声道具有时变特性且存在扰动噪声,在VFNLANC方法中设计次级通道混合辨识架构,通过离线辨识生成初始通道参数并结合在线辨识实时更新参数,可既保证辨识精度又降低计算复杂度。采用实际变压器噪声数据对所提方法进行了仿真实验,结果显示VFNLANC方法的降噪量比NANC方法提升3.2~6 dB。
基金Supported by the National Natural Science Foundation of China(11971458,11471310)。
文摘In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained from the Euler-Maruyama discretization of the underlying stochastic differential equations(SDEs),based on which the loss function is built.The stochastic gradient descent method is applied in the neural network training.Numerical experiments demonstrate the effectiveness of our method.
