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1.
SIGNAL TO NOISE RATIO (SAMPLE ASSIGNMENT)
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Adaptive noise cancellation using LMS algorithm.
This sample assignment shows a single perceptron neural network..
noisecancel.m
close all;
clear all;clc;
t=1:0.025:5;
desired=5*sin(2*3.*t);
noise=5*sin(2*50*3.*t);
refer=5*sin(2*50*3.*t+ 3/20);
primary=desired+noise;
subplot(4,1,1);
plot(t,desired);
ylabel('desired');
subplot(4,1,2);
plot(t,refer);
ylabel('refer');
subplot(4,1,3);
plot(t,primary);
ylabel('primary');
2.
order=2;
mu=0.005;
n=length(primary)
delayed=zeros(1,order);
adap=zeros(1,order);
cancelled=zeros(1,n);
for k=1:n,
delayed(1)=refer(k);
y=delayed*adap';
cancelled(k)=primary(k)-y;
adap = adap + 2*mu*cancelled(k) .* delayed;
delayed(2:order)=delayed(1:order-1);
end
subplot(4,1,4);
plot(t,cancelled);
ylabel('cancelled');
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