Le 03/02/2023 à 11:24, Heinz Nabielek a écrit :
On 03.02.2023, at 11:13, Stéphane Mottelet <stephane.motte...@utc.fr> wrote:
Thanks for the code.
Just a remark on the notations, you should write :
F(T) = Int_{0}^{T} PHI(t) . f(T-t) . dt
i.e. not F(t) since t is mute.
However, you should pay attention to the delay notion associated with
convolution and the relationships between discrete convolution and continuous
convolution. I am not sure that the output of conv matches with a given
discretization of the integral above. Maybe rectangle method, but I am not sure
at all. Anyway, you should have F(0)=0 which does not seem to be the case in
your graph.
That's expected, since for a regular convolution as performed with
conv() or convol(), the integral bounds do not depend on the delay. They
depend only on the widths of both convoluted functions.
Neither F(t) = Int_{0}^{T} PHI(t) . f(T-t) . dt
nor F(t) = Int_{0}^{T} (d PHI(t)/dt) . f(T-t) . dt are convolutions.
They are "elastically-limited" ones. That is to say: not convolutions at
all ;-)
F(t), of course.
no no, F(T), as Stéphane wrote it. After computing the integral over t,
t is used and over, and disappears from the result. Only the delay T
remains as parameter in the integrand, and as the variable in the result
F(T).
So now, your question might be likely: how is it possible to actually
compute
F(t) = Int_{0}^{T} (d PHI(t)/dt) . f(T-t) . dt
?
Here is a draft proposal:
1) build the (let's say row) vector A = (dPHI/dt) of sampled data at
sampled values t
2) build the row vector B = f(-t) of sample data at t values
3) build the matrix C of (padded) A and the matrix D of (shiffted and
padded) D = B(T-t), with T as multiples of the dt step
Each row of C and D corresponds to a T value.
4) compute E = (C .* D)*dt when dt is the time step.
5) Set to zero all elements on (#) and above the diagonal of E.
That's to cancel elements with t>T, to code the upper bound T of
the integral.
5) sum E along rows (the actual integration. You can refine with a
trapeze method).
and that's it: the resulting column vector is your F(T).
Best regards
Samuel
PS: that's an interesting problem
PS2: (#) otherwise F(0) won't be 0
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