1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
|
//
// An example of arma simulation and identification
// form ( K.J. Astrom)
// The armax process with the following characteristics
// a=[1,-2.851,2.717,-0.865]
// b=[0,1,1,1]
// d=[1,0.7,0.2]
// is simulated with an input u of a pseudo random binary type
//
// We use the simulated trajectory zd
// as an input to the armax identification macro
// The noise in the armax is coloured and armax
// will give a biaised estimator
// Copyright INRIA
a=[1,-2.851,2.717,-0.865]
b=[0,1,1,1]
d=[1,0.7,0.2]
ar=armac(a,b,d,1,1,1);
write(%io(2),"Simulation of an ARMAX process:");
ar
// The input
n=300;
u=-prbs_a(n,1,int([2.5,5,10,17.5,20,22,27,35]*100/12));
// simulation
zd=narsimul(ar,u);
xselect();xbasc();
plot2d2("enn",1,zd',[1,1],"121","Simulated output");
plot2d2("enn",1,1000*u',[3,4],"100","Input [scaled]");
halt();
write(%io(2),"Identification ARX (least square):");
[arc,la,lb,sig,resid]=armax(3,3,zd,u,1,1);
arc
halt();
// using now armax1 : coloured noise idsentification
// you can test the same example with
// n=1000; u=rand(1,n,'g') zd=narsimul(ar,u);
[arc1,resid]=armax1(3,3,2,zd(1:n),u,1);
arc1
|
