rude man said:
I meant to post also that I think I know what's with the missing U(t-2) term. Will try to send answer tomorrow.
Well, it's tomorrow! (12:32 a.m.)
OK, here's the deal:
Mathematically, the U(t-2) does not belong.
Let
h1= 2exp(-4t)
h2 = exp(2t)
x = t
2δ(t-2)
y = x*(h1*h2)
By the conventional formula for convolution, the answer is y = (4/3){[exp2(t-2)] - exp[-4(t-2)]} as we have derived. Per wikipedia:
but
so
Notice: No U(t-T) term.
On the other hand, assume h1 and h2 are the impulse responses for two separate networks connected in cascade (series). Then the impulse response for the combined network is h1*h2 = 2[exp(2t) - exp(-4t)].
Now we apply an input x = t
2δ(t-2). Since the input x is zero until t = 2, so must be the output. So when we evaluate the convolution integral y = x*(h1*h2) = (4/3){[exp2(t-2)] - exp[-4(t-2)]} we need to multiply that expression by U(t-2)
for the network since otherwise we don't get zero output for t < 2.
In other words: it depends on how the convolution integral is interpreted. If you omit the U(t-2) term you get a finite output for t < 2 which is wrong
for the network, but U(t-2) does not appear in the mathematically derived convolution integral. So, bottom line, I'd say our answer is at least as valid as theirs.
This is similar to many problems in algebra. For example, area of a square of side x = x
2. What are the sides? You can't have negative sides even though (-x)(-x) = x
2 also.