Scilab Home page | Wiki | Bug tracker | Forge | Mailing list archives | ATOMS | File exchange
Please login or create an account
Change language to: English - Français - Português - Русский -
Scilabヘルプ >> Signal Processing > Correlation Convolution > hank

hank

共分散からハンケル行列を得る

呼び出し手順

hk =hank(m, n, cov)

引数

m

ブロック行の数

n

ブロック列の数

cov

共分散の系列; 次のように指定します :[R0 R1 R2...Rk]

hk

ハンケル行列の計算値

説明

この関数は,ベクトル過程の共分散系列から 大きさ(m*d,n*d)のハンケル行列を構築します. より正確には以下となります:

この関数は,ベクトル過程の共分散系列から 大きさ(m*d,n*d)のハンケル行列を作成します. より正しくは:

//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);

参照

Scilab Enterprises
Copyright (c) 2011-2015 (Scilab Enterprises)
Copyright (c) 1989-2012 (INRIA)
Copyright (c) 1989-2007 (ENPC)
with contributors
Last updated:
Wed Jun 15 08:35:25 CEST 2016