phc - Markovian representation

Calling Sequence

[H,F,G]=phc(hk,d,r)

Parameters

• hk : hankel matrix
• d : dimension of the observation
• r : desired dimension of the state vector for the approximated model
• H, F, G : relevant matrices of the Markovian model

Description

Function which computes the matrices H, F, G of a Markovian representation by the principal hankel component approximation method, from the hankel matrix built from the covariance sequence of a stochastic process.

Examples

//
//This example may usefully be compared with the results from 
//the 'levin' macro (see the corresponding help and example)
//
//We consider the process defined by two sinusoids (1Hz and 2 Hz) 
//in additive Gaussian noise (this is the observation); 
//the simulated process is sampled at 10 Hz.
//
t=0:.1:100;rand('normal');
y=sin(2*%pi*t)+sin(2*%pi*2*t);y=y+rand(y);plot(t,y)
//
//covariance of y
//
nlag=128;
c=corr(y,nlag);
//
//hankel matrix from the covariance sequence
//(we can choose to take more information from covariance
//by taking greater n and m; try it to compare the results !
//
n=20;m=20;
h=hank(n,m,c);
//
//compute the Markov representation (mh,mf,mg)
//We just take here a state dimension equal to 4 :
//this is the rather difficult problem of estimating the order !
//Try varying ns ! 
//(the observation dimension is here equal to one)
ns=4;
[mh,mf,mg]=phc(h,1,ns);
//
//verify that the spectrum of mf contains the 
//frequency spectrum of the observed process y
//(remember that y is sampled -in our example 
//at 10Hz (T=0.1s) so that we need 
//to retrieve the original frequencies through the log 
//and correct scaling by the frequency sampling)
//
s=spec(mf);s=log(s);
s=s/2/%pi/.1;
//
//now we get the estimated spectrum
imag(s),
//
 

See Also

levin