WienHammerIdentNonPar2

WienHammerIdentNonPar2 - % Nonparametric identification of...

Info iconThis preview shows pages 1–2. Sign up to view the full content.

View Full Document Right Arrow Icon
%% Nonparametric identification of wiener hammerstein system. % Obtain inputs and outputs of model clear all u=normrnd(0,2,1,500); % A white gaussian input sequence u with length %400 0 mean and standard deviation 2 ut=normrnd(0,2,1,200); %input for testing. e=normrnd(0,.2,1,400); % A white gaussian with zero mean and standart de %viation .2 with length 400. it is error term e = zeros(1,400); % this is added after all. actually it should have % been done before v = zeros(1,500); w = zeros(1,500); y = zeros(1,500); K = zeros(200,200);Ker = zeros(200,200); N = 200; r = 10; ny = 1; nu = 2; sg =6; % N: # of training data a = [.7;.5]; b = [.8;.3]; % u = zeros(1,500);u(1,1) = 1; x = zeros(ny+1+nu,500); xr = zeros(ny+1+nu,500); %regression vectors. for k = 1 : 497 v(k+1) = a(1)*v(k) + a(2)*u(k); w(k+1) = sin(v(k+1)); %v(k+1)/(1+v(k+1)); % y(k+2) = b(1)*y(k+1) + b(2)*w(k+1); if( k>r) x(:,k) = [y(k:-1:k-ny) u(k-1:-1:k-nu)]'; end end %% construct kernel and other submatrices % Kernel matrix for i = 1:200
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Image of page 2
This is the end of the preview. Sign up to access the rest of the document.

Page1 / 2

WienHammerIdentNonPar2 - % Nonparametric identification of...

This preview shows document pages 1 - 2. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online