This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English version of the page.

Note: This page has been translated by MathWorks. Click here to see
To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

Adaptive Noise Cancellation

A linear neuron is allowed to adapt so that given one signal, it can predict a second signal.

TIME defines the time steps of this simulation. P defines a signal over these time steps. T is a signal derived from P by shifting it to the left, multiplying it by 2 and adding it to itself.

time = 1:0.01:2.5;
X = sin(sin(time).*time*10);
P = con2seq(X);
T = con2seq(2*[0 X(1:(end-1))] + X);

Here is how the two signals are plotted:

title('Input and Target Signals')

The linear network must have tapped delay in order to learn the time-shifted correlation between P and T. NEWLIN creates a linear layer. [-3 3] is the expected input range. The second argument is the number of neurons in the layer. [0 1] specifies one input with no delay and one input with a delay of one. The last argument is the learning rate.

net = newlin([-3 3],1,[0 1],0.1);

ADAPT simulates adaptive networks. It takes a network, a signal, and a target signal, and filters the signal adaptively. Plot the output Y in blue, the target T in red and the error E in green. By t=2 the network has learned the relationship between the input and the target and the error drops to near zero.

plot(time,cat(2,Y{:}),'b', ...
   time,cat(2,T{:}),'r', ...
   time,cat(2,E{:}),'g',[1 2.5],[0 0],'k')

Was this topic helpful?