MATLAB: Cumulative RMS in Matlab

cumulativeerror handlingMATLABrms

Hi,

I'm trying to find the cumulative RMS of a vector. (The data happens to be model error – labelled devCoM for deviation of centre of mass. The calculation should get less accurate as time proceeds, so cumulative error is important.)

I can find the cumulative sum by taking

cumsum(devCoM)

…but this fluctuates about zero.

I can find the RMS of the entire dataset by taking

sqrt(sum(devCoM.^2)

…but this doesn't reveal the expected increase in error over time.

Is there some way I could combine these methods?

Thanks,

Frederick

Best Answer

Start with a mathematical definition of what you want to achieve.

I guess you want this:

x = rand(1, 100);  % Your data
% "Cumulative root mean square"
y = sqrt(cumsum(x.*x) ./ (1:numel(x)))

So the cumulative concerns the mean of the squared elements only, correct?

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x = [a;b;c;d];
cumsum(x) ./ (1:numel(x))

MATLAB: Fit distribution to probability plot

It looks like you are looking at comparing the Cumulative Distribution Function (CDF) with the Empirical Cumulative Distribution Function (ECDF) which is not the same as a probability plot.

I'll start by generating some random numbers from an EVD as I do not have your data:

rng('default')
a = gevrnd(0,107,399,100,1);

Next we need to fit the parameters of the distribution:

Mdl = fitdist(a,'GeneralizedExtremeValue'); % Returns a PD object (Requires MATLAB later than 2009)

You can then calculate the ECDF: [f,x] = ecdf(a); % plot(x,f)

Then calculate the CDF implied by the fitted parameters:

y = cdf(Mdl,x);
hold on
plot(x,y)
hold off

If you indeed wanted to look at a probability plot, you should know that by default probplot compares the data with a normal distribution. To change this, pass in the name of the distribution:

figure
probplot('extreme value',a)

Best Answer

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