ghdawn

w1=(rand(3,2)-0.5).*2+0.1;
w2=(rand(3,1)-0.5).*2+0.1;
ita=1;
alpha=1;

%x=[1 1;1 3;1 2;1 4];
%x=[ones(4,1).*(-1) x];

x=[0 0;1 1; 1 0; 0 1];
h=[0 0 0 0]';

ghdawn

function [w,flag] = learning(flag,w,omega,target )
%UNTITLED3 Summary of this function goes here
%   Detailed explanation goes here
    ita=1;
    result=(omega*w)>0;
    
    result=target-result;
    for i = 1 : 3
        if result(i) == 0 
            continue

ghdawn

% x : state vector [pos;speed]
% X : estimate vector
% n : noise vector - Q : cov.matrix
% z : measurement vector 
% v : measurement noise - R : cov.matrix
% 
% F_x : transition matrix
% F_n: noise matrix
% H : measurement matrix