c=(exp((-z​^2)/(2*sig​ma^2)))+mn​oise

How do you add mnoise which is a 251 data with normal distribution and derivation 0.02. When I added mnoise It says there is an error with + matrices

Answer 1

[Amit]

mnoise = 0.02*randn(251,1);

[Ajay Joseph]

Sorry amit my questions was that I think I forgot to add the rest of the question c=(exp((-z^2)/(2*sigma^2)))+mnoise and when I add mnoise there is an error

[Amit]

that might have been that the dimension of first term and second term were different.

What was the error? Matlab generally tell the error. Can you post that.

Also, can you add an example for z, sigma and mnoise values.

[Ajay Joseph]

Error using + Matrix dimensions must agree. This was the error

The s value is just a linear number like 2.31 The z value is a matix z=linspace(-7,7,251) Mnoise is = 0.02*randn(251,1)

[Amit]

Here, the issue is

z = linspace(-7,7,251); % This is a row vector
mnoise = 0.02*randn(1,251); % Now we made this a row vector as well

This time this will work :)

[Ajay Joseph]

Damn thanks man The simplest things makes a big difference

Answer 2

[John D'Errico]

It appears you have not realized there is a difference between row and column vectors. This is a common mistake, since a vector is just a vector, right? In fact, MATLAB sees a vector as just an array with the row or column dimension equal to 1. That makes a lot of sense in a linear algebra context, which is really where MATLAB comes from.

LINSPACE (like COLON) creates row vectors, so an array of size 1 by n.

A good way to add noise to an array or vector without bothering to know the size is...

y = y + randn(size(y)).*0.02;

RANDN by default generates Gaussian deviates with mean zero and standard deviation 1. Multiplying by 0.02 scales them to have standard deviation 0.02.

[Ajay Joseph]

thanks but I guess Amit answer works aswell