I have some collected data that should be modeled by a damped oscillation. Here are the data in a list of ordered pairs:

data = {{60, 1.1}, {180, 27.7}, {300, 44.5}, {420, 37.1}, {540, 17.5}, {660, 7.6}, {780, 15.25}, {900, 29.3}, {1020, 34.5}, {1140, 27.5}, {1260, 17.7}, {1380, 15.25}, {1500, 21.25}, {1620, 28.1}}

Indeed, a plot of the data shows that this looks to be a good fit:

I’m trying to fit a damped sine wave of the form

A*Exp[-k*t]*Sin[w*t + p] + h

I tried to do this in Mathematica, generally following the documentation on NonlinearModelFit. I used the following code:

model = A*Exp[-k*t]*Sin[w*t + p] + h;

f = NonlinearModelFit[data, model, {A, k, w, p, h}, t];

Normal[f]

When I run this, however, I get the following function:

5.50389 – 6.06739 E^(0.202624 t) Sin[2.00518 + 0.485183 t]

Plotted, it looks like this:

That doesn’t seem to be an appropriate fit for the data. Does anyone have an idea as to how I can produce a better model? Thanks!

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– bbgodfrey

Feb 20 ’15 at 5:50

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1 Answer

1

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Finding parameter starting estimates is an important starting point.

I post this for illustration. There are many ways to approximate.

Manipulate[

Show[ListPlot[data],

Plot[s Exp[- a t] Sin[ b t + c] + f, {t, 0, 1000}]], {a, 0,

0.1}, {b, 0.001, 0.01}, {c, 1, 10}, {s, 20, 50}, {f, 5, 50}]

Now fit model:

nlm = NonlinearModelFit[data,

amp Exp[- k t] Sin[ a t + b] +

c, {{amp, 20}, {k, 0.0006}, {a, 0.009}, {b, 20}, {c, 25}}, t]

Show[ListPlot[data], Plot[nlm[t], {t, 0, 1600}],

PlotLabel -> Framed[nlm[t]], Frame -> True]

+1 sweet answer!

– ciao

Feb 20 ’15 at 6:08

1

That was really helpful. I didn’t know how to set those starting estimates. Thank you so much!

– bradford412

Feb 20 ’15 at 23:56