Hello,
Le 01/11/2018 à 09:27, Heinz Nabielek a écrit :
Sorry, Scilab friends, I am still not fluid with vector operations.
Can someone rewrite the for loop for me into something much more efficient?
n=1000;
Z=grand(1,n,'nor',0,1);
r=0.9;
V=Z;
for i=2:n;
V(i)=r*V(i-1)+sqrt(1-r^2)*Z(i);
end;
The transformation generates an autocorrelated (here rho=0.9) normal
distribution V from an uncorrelated normal distribution Z and eventually I will
need it for very much larger n values....
You may use filter(), with a feedback component (since V(i) depends on
the previous state V(i-1) computed at the previous step).
However, as shown below, a quick trial shows an initial discrepancy
between filter() result and yours with the explicit loop.
I don't know why. May be the setting for the initial condition should be
carefully considered/tuned...
n=1000;
Z=grand(1,n,'nor',0,1);
r=0.9;
V=Z;
for i=2:n;
V(i)=r*V(i-1)+sqrt(1-r^2)*Z(i);
end;
y = filter(sqrt(1-r^2),[1 -r], Z);
clf
subplot(3,1,1), plot(Z), ylabel('input Z')
subplot(3,1,2), plot(V), ylabel('V')
d = abs(y-V); d(d==0) = %nan;
subplot(3,1,3), plot2d("nl",d), title('filter() - V')
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