# How to fit data to a convolution equation

How to fit data to a convolution equation:

m1data = Import["http://pastebin.com/raw/GckJAgpY", "Table"];
m2data = Import["http://pastebin.com/raw/5HvwSg7i", "Table"];
ListLinePlot[{m1data,m2data},PlotRange-> All] Objective function:

m2[t] == m1[t] + a1*Integrate[m1[tau]*Exp[-a2 (t - tau)], {tau, 0, t}]


Is it possible to estimate a1 and a2 from the data?

• You mean without proposing a model for m1data? – Dr. belisarius Dec 31 '15 at 2:26
• @Dr.belisarius not sure, we have input and output data, can it be possible. I will try to add the model for m1data. – Johan Dec 31 '15 at 2:32
• Do you mean m1[tau] or m1[t] ? If the former, do you not need to give the definition of m1[tau] in the solution of the ODE ? – JimB Dec 31 '15 at 4:53
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## 1 Answer

I wasn't sure how to set it up with the standard fit functions, so I just rolled my own least squares. (It probably can be done, but I had an inkling I might want to have greater control over the computation. I hope it helps.)

I start by defining m1 and m2 on your data:

Clear[m1, m2];
(m1[t_] /; t == First@# = Last@#) & /@ m1data;
(m2[t_] /; t == First@# = Last@#) & /@ m2data;


Then I defined the convolution, using the trapezoid rule and based on a consisted delta t of 0.1 in both data sets.

ClearAll[ni];
SetAttributes[ni, Listable];
ni[0. | 0, _] := 0.;
mem : ni[t_?NumericQ, a2_?NumericQ] := mem = Quiet[
NIntegrate[m1[tau]*Exp[-a2 (t - tau)], {tau, 0, t},
Method -> {"TrapezoidalRule", "Points" -> 1 + Round[t/0.1]},
MaxRecursion -> 0],
NIntegrate::ncvb]


The objective function is the sum of squares of the residuals the solution to your ODE. This is to be minimized.

obj = m2[t] - (m1[t] + a1*ni[t, a2]) /. {m1[t] -> m1data[[All, 2]],
m2[t] -> m2data[[All, 2]], t -> m1data[[All, 1]]} // #.# &;


The value for a1 is consistent, but the model is not very sensitive to a2. It's always a big value, which suggests only t = tau is contributing much to the convolution.

FindMinimum[obj, {{a1, -1.}, {a2, 100}}]
(*  {1.20002, {a1 -> -20.173, a2 -> 266.816}}  *)

FindMinimum[obj, {{a1, -13.}, {a2, 1550}}]
(*  {1.20002, {a1 -> -20.173, a2 -> 1550.}}  *)

FindMinimum[obj, {{a1, -20.}, {a2, 50}}]
(*  {1.20002, {a1 -> -20.173, a2 -> 2250.22}}  *)

FindMinimum[obj2, {{a1, -53.}, {a2, 4500}}]
(*  {1.20002, {a1 -> -20.173, a2 -> 4500.}}  *)


Note: The trapezoid rule in NIntegrate uses Romberg quadrature. If you turn it off, then a1 is consistently -13.4487 and a2 is very large just as before.