# 【基础处理】基于matlab Fxlms算法有源噪声控制系统【含Matlab源码 1394期】

·  阅读 1308

## 二、部分源代码

``````%--------------------------------------------------------------------------

%--------------------------------------------------------------------------

% Set simulation duration (normalized)
clear
T=1000;

% We do not know P(z) and S(z) in reality. So we have to make dummy paths
Pw=[0.01 0.25 0.5 1 0.5 0.25 0.01];
Sw=Pw*0.25;

% Remember that the first task is to estimate S(z). So, we can generate a
% white noise signal,

% send it to the actuator, and measure it at the sensor position,

% Then, start the identification process
Shx=zeros(1,16);     % the state of Sh(z)
Shw=zeros(1,16);     % the weight of Sh(z)
e_iden=zeros(1,T);   % data buffer for the identification error

% and apply least mean square algorithm
mu=0.1;                         % learning rate
for k=1:T,                      % discrete time k

Shy=sum(Shx.*Shw);	        % calculate output of Sh(z)
e_iden(k)=y_iden(k)-Shy;    % calculate error
end

% Lets check the result
subplot(2,1,1)
plot([1:T], e_iden)
ylabel('Amplitude');
xlabel('Discrete time k');
legend('Identification error');
subplot(2,1,2)
stem(Sw)
hold on
stem(Shw, 'r*')
ylabel('Amplitude');
xlabel('Numbering of filter tap');
legend('Coefficients of S(z)', 'Coefficients of Sh(z)')

% The second task is the active control itself. Again, we need to simulate
% the actual condition. In practice, it should be an iterative process of
% 'measure', 'control', and 'adjust'; sample by sample. Now, let's generate
% the noise:
X=randn(1,T);

% and measure the arriving noise at the sensor position,

% Initiate the system,
Cx=zeros(1,16);       % the state of C(z)
Cw=zeros(1,16);       % the weight of C(z)
Sx=zeros(size(Sw));   % the dummy state for the secondary path
e_cont=zeros(1,T);    % data buffer for the control error
Xhx=zeros(1,16);      % the state of the filtered x(k)

% and apply the FxLMS algorithm
mu=0.1;                            % learning rate
for k=1:T,                         % discrete time k
Cx=[X(k) Cx(1:15)];            % update the controller state
Cy=sum(Cx.*Cw);                % calculate the controller output
Sx=[Cy Sx(1:length(Sx)-1)];    % propagate to secondary path
e_cont(k)=Yd(k)-sum(Sx.*Sw);   % measure the residue

end

% Report the result
figure
subplot(2,1,1)
plot([1:T], e_cont)
ylabel('Amplitude');
xlabel('Discrete time k');
legend('Noise residue')
subplot(2,1,2)
plot([1:T], Yd)
hold on
plot([1:T], Yd-e_cont, 'r:')
ylabel('Amplitude');
xlabel('Discrete time k');
legend('Noise signal', 'Control signal')

## 四、matlab版本及参考文献

1 matlab版本 2014a

2 参考文献 [1]韩纪庆,张磊,郑铁然.语音信号处理（第3版）[M].清华大学出版社，2019. [2]柳若边.深度学习:语音识别技术实践[M].清华大学出版社，2019. [3]龚孝平,郭勇,刘强,朱再胜.驾驶室主动降噪的改进FxLMS算法及DSP实现[J].传感器与微系统. 2021,40(09