njit-etd2003-081 - New Jersey Institute of Technology

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4. Remove the constant levels and make the data zero mean with
z2 = dtrend(z2);
5. Now, fit to the data a model of the form:
where T is the sampling interval (here 0.005 seconds). This model, known as an ARX
model, tries to explain or compute the value of the output at time t, given previous values
of y and u.
The best values of the coefficients a l , a 2 , b 1 and b2 can be computed with
th = arx(z2,[10 10 31);
6. The numbers in the second argument tell arx to find a model (B.1) with ten a-
parameters, ten b-parameters, and three delays. The result is stored in the matrix th in a
somewhat coded form. To specify the actual sampling interval, enter
th = sett(th,0.005);
7. There are several ways to display and illustrate the computed model. With
present(th);
the coefficient values of (B.1) and their estimated standard deviations are presented on
the screen.
8. Next, one might ask how to evaluate how well the model fits the data. A simple test
is to run a simulation whereby real input data is fed into the model, and compare the
simulated output with the actual measured output. For this, select a portion of the data
that was not used to build the model. for example, from sample 25.101 to in NM.