covariance to hankel matrix
hk =hank(m, n, cov)
:m number of bloc-rows : :n number of bloc-columns : :cov sequence of covariances; it must be given as :[R0 R1 R2...Rk] : :hk computed hankel matrix :
This function builds the hankel matrix of size (m*d,n*d) from the covariance sequence of a vector process. More precisely:
This function builds the hankel matrix of size (m*d,n*d) from the covariance sequence of a vector process. More precisely:
//Example of how to use the hank macro for
//building a Hankel matrix from multidimensional
//data (covariance or Markov parameters e.g.)
//
//This is used e.g. in the solution of normal equations
//by classical identification methods (Instrumental Variables e.g.)
//
//1)let's generate the multidimensional data under the form :
// C=[c_0 c_1 c_2 .... c_n]
//where each bloc c_k is a d-dimensional matrix (e.g. the k-th correlation
//of a d-dimensional stochastic process X(t) [c_k = E(X(t) X'(t+k)], '
//being the transposition in scilab)
//
//we take here d=2 and n=64
c = `rand`_(2, 2 * 64)
//generate the hankel matrix H (with 4 bloc-rows and 5 bloc-columns)
//from the data in c
H = hank(4, 5, c);