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Comment:Sanity check on y_0
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SHA3-256: ffbec8fe833f8934d54e2428489f76b91c96e3e68121d7a3ef02ce7367f9f90f
User & Date: gawthrop@users.sourceforge.net on 2002-05-20 13:32:36
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Context
2002-05-20
13:42:31
Uses simpar.first for first printed output check-in: 8953950403 user: gawthrop@users.sourceforge.net tags: origin/master, trunk
13:32:36
Sanity check on y_0 check-in: ffbec8fe83 user: gawthrop@users.sourceforge.net tags: origin/master, trunk
10:29:59
-oct now forces an m target language to be oct
(if the rep is an the list: "ae input logic numpar simpar state") check-in: 8da889c6d5 user: gawthrop@users.sourceforge.net tags: origin/master, trunk
Changes
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Modified mttroot/mtt/lib/control/PPP/ppp_optimise.m from [bf1568c82a] to [d6b999b722].

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  ######################################
  
  ###############################################################
  ## Version control history
  ###############################################################
  ## $Id$
  ## $Log$
  ## Revision 1.10  2002/05/13 16:01:09  gawthrop
  ## Addes Q weighting matrix
  ##
  ## Revision 1.9  2002/05/08 10:14:21  gawthrop
  ## Idetification now OK (Moved data range in ppp_optimise by one sample interval)
  ##
  ## Revision 1.8  2002/04/23 17:50:39  gawthrop
  ## error --> err to avoid name clash with built in function
  ##
  ## Revision 1.7  2001/08/10 16:19:06  gawthrop
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    [y,y_par,x] = eval(sim_command); # Simulate
    [N_data,N_y] = size(y);

    if (N_y!=n_y)
      mess = sprintf("n_y (%i) in data not same as n_y (%i) in model", n_y,N_y);
      error(mess);
    endif
    

    if ( (N_data-n_data)<1 )
      error(sprintf("y_0 (%i) must be shorter than y (%i)", n_data, N_data));
    endif
    
    ## Use the last part of the simulation to compare with data
    ## And shift back by one data point
    y = y(N_data-n_data:N_data-1,:);
    y_par = y_par(N_data-n_data:N_data-1,:);

    ##Evaluate error, cost derivative J and cost second derivative JJ
    err = 0; 
    J = zeros(n_th,1);
    JJ = zeros(n_th,n_th);
    
   
    for i = 1:n_y
      E = y(:,i) - y_0(:,i);	#  Error in ith output
      err = err + Q(i)*(E'*E);	# Sum the squared error over outputs
      y_par_i = y_par(:,i:n_y:n_y*n_th); # sensitivity function (ith output)
      J  = J + Q(i)*y_par_i'*E;	# Jacobian
      JJ = JJ + Q(i)*y_par_i'*y_par_i; # Newton Euler approx Hessian
    endfor
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