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Comment:Restuctured to be more logical.
Data is now in columns to be compatible with MTT.
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SHA3-256: 0ea7b213e72c3dbe876fdee0f3027988d164a9fa5dc5f99694c8110013341d79
User & Date: gawthrop@users.sourceforge.net on 2001-04-04 08:36:25
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Context
2001-04-04
10:05:38
Reresentation for system identification for ppp check-in: 34531ed402 user: gawthrop@users.sourceforge.net tags: origin/master, trunk
08:36:25
Restuctured to be more logical.
Data is now in columns to be compatible with MTT. check-in: 0ea7b213e7 user: gawthrop@users.sourceforge.net tags: origin/master, trunk
01:03:01
Fixed elimination of 0 in xxx(0...) when ... does not start with a digit. check-in: 58203f3d83 user: geraint@users.sourceforge.net tags: origin/master, trunk
Changes
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Modified mttroot/mtt/lib/control/PPP/ppp_optimise.m from [aff901223d] to [55529a32e0]. 

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function [par,Par,Error,Y,iterations] = ppp_optimise(system_name,x_0,u,t,par_0,free,y_0,extras);


  ## Usage: [par,Par,Error,Y,iterations] = ppp_optimise(system_name,x_0,u,t,par_0,free,y_0,weight,extras);
  ##  system_name     String containg system name
  ##  y_s   actual system output
  ##  theta_0   initial parameter estimate
  ##  free  Indices of the free parameters within theta_0
  ##  weight Weighting function - same dimension as y_s








  ##  extras.criterion convergence criterion
  ##  extras.max_iterations limit to number of iterations
  ##  extras.v  Initial Levenberg-Marquardt parameter
















  ## Copyright (C) 1999,2000 by Peter J. Gawthrop


  ## Extract indices
  i_t = free(:,1); # Parameters
  i_s = free(:,2)'; # Sensitivities

  if nargin<8
    extras.criterion = 1e-5;
    extras.max_iterations = 10;
    extras.v = 1e-5;
    extras.verbose = 0;
  endif
  

  [n_y,n_data] = size(y_0);

  n_th = length(i_s);
  error_old = inf;
  error_old_old = inf;
  error = 1e50;
  reduction = 1e50;
  par = par_0;

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function [par,Par,Error,Y,iterations] = \
      ppp_optimise(system_name,x_0,par_0,simpar,u,y_0,free,extras);
  ## Levenberg-Marquardt optimisation for PPP/MTT
  ## Usage: [par,Par,Error,Y,iterations] = ppp_optimise(system_name,x_0,par_0,simpar,u,y_0,free[,extras]);
  ##  system_name     String containing system name
  ##  x_0             Initial state
  ##  par_0           Initial parameter vector estimate
  ##  simpar          Simulation parameters:
  ##        .first      first time
  ##        .dt         time increment
  ##        .stepfactor Euler integration step factor
  ##  u               System input (column vector, each row is u')
  ##  y_0             Desired model output
  ##  free            one row for each adjustable parameter
  ##                  first column parameter indices
  ##                  second column corresponding sensitivity indices
  ##  extras (opt)    optimisation parameters
  ##        .criterion convergence criterion
  ##        .max_iterations limit to number of iterations
  ##        .v  Initial Levenberg-Marquardt parameter

  ###################################### 
  ##### Model Transformation Tools #####
  ######################################
  
  ###############################################################
  ## Version control history
  ###############################################################
  ## $Id$
  ## $Log$
  ## Revision 1.1  2000/12/28 11:58:07  peterg
  ## Put under CVS
  ##
  ###############################################################


  ## Copyright (C) 1999,2000 by Peter J. Gawthrop

  sim_command = sprintf("%s_ssim(x_0,par,simpar,u,i_s)", system_name)
  ## Extract indices
  i_t = free(:,1); # Parameters
  i_s = free(:,2)'; # Sensitivities

  if nargin<9
    extras.criterion = 1e-5;
    extras.max_iterations = 10;
    extras.v = 1e-5;
    extras.verbose = 0;
  endif
  

  [n_data,n_y] = size(y_0);

  n_th = length(i_s);
  error_old = inf;
  error_old_old = inf;
  error = 1e50;
  reduction = 1e50;
  par = par_0;
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  while (abs(reduction)>extras.criterion)&&\
	(abs(error)>extras.criterion)&&\
	(iterations<extras.max_iterations)

    iterations = iterations + 1; # Increment iteration counter

    [y,y_par] = eval(sprintf("%s_sim(x_0,u,t,par,i_s)", system_name)); # Simulate

    ##Evaluate error, cost derivative J and cost second derivative JJ
    error = 0; 
    J = zeros(n_th,1);
    JJ = zeros(n_th,n_th);
    
    for i = 1:n_y
      E = y(i,:) - y_0(i,:);	#  Error
      error = error + (E*E');	# Sum the error over outputs
      y_par_i = y_par(i:n_y:n_y*n_th,:);
      J  = J + y_par_i*E';	# Jacobian
      JJ = JJ + y_par_i*y_par_i'; # Newton Euler approx Hessian (with LM
				# term
    endfor

    if iterations>1 # Adjust the Levenberg-Marquardt parameter
      reduction = error_old-error;
      predicted_reduction =  2*J'*step + step'*JJ*step;
      r = predicted_reduction/reduction;
      if (r<0.25)||(reduction<0)








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  while (abs(reduction)>extras.criterion)&&\
	(abs(error)>extras.criterion)&&\
	(iterations<extras.max_iterations)

    iterations = iterations + 1; # Increment iteration counter

    [y,y_par] = eval(sim_command); # Simulate

    ##Evaluate error, cost derivative J and cost second derivative JJ
    error = 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
      error = error + (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 + y_par_i'*E;	# Jacobian
      JJ = JJ + y_par_i'*y_par_i; # Newton Euler approx Hessian

    endfor

    if iterations>1 # Adjust the Levenberg-Marquardt parameter
      reduction = error_old-error;
      predicted_reduction =  2*J'*step + step'*JJ*step;
      r = predicted_reduction/reduction;
      if (r<0.25)||(reduction<0)
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	printf("  ratio:      %g\n", r);
	printf("  L-M param:  %g\n", v);
	printf("  parameters: ");
	for i_th=1:n_th
	  printf("%g ", par(i_t(i_th)));
	endfor
	printf("\n");
	  
      endif
    
      if reduction<0		# Its getting worse
	par(i_t) = par(i_t) + step; # rewind parameter
	error = error_old;	# rewind error
	error_old = error_old_old; # rewind old error
	if extras.verbose
	  printf(" Rewinding ....\n");
	endif
	
      endif

    endif

    ## Compute step using pseudo inverse
    JJL = JJ + v*eye(n_th);	# Levenberg-Marquardt term
    step =  pinv(JJL)*J;	# Step size

    par(i_t) = par(i_t) - step; # Increment parameters
    error_old_old = error_old;	# Save old error
    error_old = error;		# Save error

    ##Some diagnostics
    Error = [Error error];	# Save error
    Par = [Par par];		# Save parameters
    Y = [Y; y];			# Save output

  endwhile

endfunction













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	printf("  ratio:      %g\n", r);
	printf("  L-M param:  %g\n", v);
	printf("  parameters: ");
	for i_th=1:n_th
	  printf("%g ", par(i_t(i_th)));
	endfor
	printf("\n");

      endif
    
      if reduction<0		# Its getting worse
	par(i_t) = par(i_t) + step; # rewind parameter
	error = error_old;	# rewind error
	error_old = error_old_old; # rewind old error
	if extras.verbose
	  printf(" Rewinding ....\n");
	endif

      endif

    endif

    ## Compute step using pseudo inverse
    JJL = JJ + v*eye(n_th);	# Levenberg-Marquardt term
    step =  pinv(JJL)*J;	# Step size

    par(i_t) = par(i_t) - step; # Increment parameters
    error_old_old = error_old;	# Save old error
    error_old = error;		# Save error

    ##Some diagnostics
    Error = [Error error];	# Save error
    Par = [Par par];		# Save parameters
    Y = [Y y];			# Save output

  endwhile

endfunction

MTT: Model Transformation Tools
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