home *** CD-ROM | disk | FTP | other *** search
- %----------------------------------------------------------------------------
- % DEMONSTRATION PROGRAM-2
- % FOR MMLE.M MAXIMUM LIKELIHOOD IDENTIFICATION FUNCTION
- % IDENTIFIES 2*2 TRANSITION MATRIX WITH SINGLE DUMMY STATE
- % - A QUADRATIC COST FUNCTION EXAMPLE TO CHECK NEWTON ALGO CONVERGENCE -
- %----------------------------------------------------------------------------
-
- format compact,clc
- diary ml_demo2.log
-
- '-----------------------------------------------------------------------------';
- rule=ans;disp(ans)
- disp(' MMLE DEMONSTRATION No 2')
- disp(' No dynamics, 2 by 2 mixing matrix')
- disp(' True parameters are [ 1 1 1 1 ]')
- %--------------------------------------------------------------------------
-
- p2snam='ml_p2ss2';% FOR DEMO_2 WE GENERATE SIMULATED DATA
- ptrue=[1 1 1 1];
- [a,phi,gam,c,d]=eval([p2snam,'(ptrue)']);% GET SIMULATED SYSTEM
-
- uydata=ones(100,4);% CREATE AN INPUT
- uydata(1:50,1)=zeros(50,1);
-
- randn('seed',0);% INITIALIZE RANDOM GENERATOR
-
- uydata(:,3:4)=dlsim(phi,gam,c,d,uydata(:,1:2))+randn(100,2)/100;% SIMULATE
-
-
- % Note: IF WE DON'T ADD SOME NOISE OR MODELING ERROR, THE RESPONSE FIT WILL
- % BECOME PERFECT AND THE INNOVATIONS COVARIANCE WILL BECOME ZERO TO
- % CAUSE NUMERICAL PROBLEMS IN THE gg ESTIMATION PHASE. EITHER ADD
- % NOISE OR DON'T ESTIMATE gg ON SIMULATED DATA. THIS PROBLEM ONLY
- % OCCURS WITH SIMULATED DATA.
-
- disp(rule)
-
- % IN ADDITION TO uydata AND p2snam (DEFINED ABOVE), MUST CHOOSE VALUES FOR :
-
- gg0=eye(2);
- p0=[0 0 0 0];
- pert=.001;
- pidq=1;
- pidm=[1:4];
- pidf=[1:4];
- opt=[0 5 5 5 .02 .01 .001 1];
- mmle
- diary off
- %------------------------------------------------------------- end ml_demo2.m
-
-