Index: mttroot/mtt/lib/control/PPP/ppp_RT_sim_compute.m
==================================================================
--- mttroot/mtt/lib/control/PPP/ppp_RT_sim_compute.m
+++ mttroot/mtt/lib/control/PPP/ppp_RT_sim_compute.m
@@ -17,10 +17,10 @@
   u_star = ppp_ustar(A_u_sim,1,t,0,0,n_u-n_U);
 
   ## Simulate
   par(i_ppp_sim(:,3)) = U;		# Update the simulation ppp weights
   [y_sim,x] = eval(sprintf("%s_sim(x_0_sim, par, simpar_sim, u_star);", \
-			     system_name_sim));
-  x_0_sim  = x(n_t,:)';     # Extract state for next time
+			   system_name_sim));
+  x_0_sim  = x(n_t,:)';		# Extract state for next time
   u_sim = (u_star*U);
 endfunction
 

Index: mttroot/mtt/lib/control/PPP/ppp_nlin_run.m
==================================================================
--- mttroot/mtt/lib/control/PPP/ppp_nlin_run.m
+++ mttroot/mtt/lib/control/PPP/ppp_nlin_run.m
@@ -37,14 +37,15 @@
     extras.emulate_timing = 0;
     extras.max_iterations = 10;
     extras.simulate = 1;
     extras.v = 1e-5;
     extras.verbose = 0;
+    extras.visual = 0;
   endif
 
   ##Estimate if we have some adjustable parameters
-  estimating_parameters = (length(i_par)>0)
+  estimating_parameters = (length(i_par)>0);
   
   ## Names
   s_system_name = sprintf("s%s", system_name);
 
   ## System details -- defines simulation within ol interval
@@ -141,11 +142,11 @@
       u_star_t = ppp_ustar(A_u,1,t_ol',0,0,n_u-n_U);
 
       ## Tune parameters/states
       if (estimating_parameters==1)
 	## Save up the estimated parameters
-	par_est = pars(i_par(:,1))
+	par_est = pars(i_par(:,1));
 	p = [p; par_est'];
 
 	## Set up according to interval length
 	if (T_ol>T_ol_0) ## Truncate data
 	  simpar_est.last = T_ol_0;
@@ -160,11 +161,17 @@
 	
 	## Optimise
 	tick = time;
 	[pars,Par,Error,Y,its] = \
 	    ppp_optimise(s_system_name,x_0_models,pars,simpar_est,u_star_t,y_est,i_par,extras);
-II = [1:length(y_est)]; plot(II,y_est,"*", II,Y)
+	
+	if extras.visual
+	  figure(2);
+	  title("Parameter optimisation"); 
+	  II = [1:length(y_est)]; plot(II,y_est,"*", II,Y);
+	endif
+	
 	est_time = time-tick;  
 	t_est = [t_est;est_time];
 	its_est = [its_est; its-1];
       endif
 
@@ -178,10 +185,11 @@
       simpar_model.last = T_ol;
       [y_model,x_model] = eval(sprintf("%s_sim(x_0_model, par, simpar_model, \
  					       u_star_t);",system_name));
 
       x_0 = x_model(n_ol+1,:)';	# Initial state of next interval
+##      x_0 = x_model(n_ol-1,:)';	# Initial state of next interval
       x_0_model = x_0;
       x_0_models(1:2:(2*n_x)-1) = x_0_model;
 
       ## Compute U by optimisation
       tick = time;
@@ -196,11 +204,16 @@
       ## Optimize for next interval      
       U_old = U;		# Save previous value
       U = expm(A_u*T_ol)*U;	# Initialise from continuation trajectory
       pars(i_ppp(:,1)) = U;	# Put initial value of U into the parameter vector
       [U, U_all, Error, Y, its] = ppp_nlin(system_name,x_nexts,pars,simpars,u_star_tau,w_s,i_ppp,extras);
-
+      if extras.visual
+	figure(3);
+	title("PPP optimisation");
+	II = [1:length(w_s)]; plot(II,w_s,"*", II,Y);
+	figure(1);
+	endif
 
       ppp_time = time-tick;  
       t_ppp = [t_ppp;ppp_time];
       its_ppp = [its_ppp; its-1];
 

Index: mttroot/mtt/lib/control/PPP/ppp_optimise.m
==================================================================
--- mttroot/mtt/lib/control/PPP/ppp_optimise.m
+++ mttroot/mtt/lib/control/PPP/ppp_optimise.m
@@ -26,10 +26,13 @@
   ###############################################################
   ## Version control history
   ###############################################################
   ## $Id$
   ## $Log$
+  ## 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
   ## Tidied up the optimisation stuff
   ##
   ## Revision 1.6  2001/07/03 22:59:10  gawthrop
   ## Fixed problems with argument passing for CRs
@@ -52,11 +55,10 @@
   ##
   ###############################################################
 
 
   ## 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
@@ -116,18 +118,13 @@
       mess = sprintf("n_y (%i) in data not same as n_y (%i) in model", n_y,N_y);
       error(mess);
     endif
     
     ## Use the last part of the simulation to compare with data
-    y = y(1+N_data-n_data:N_data,:);
-    y_par = y_par(1+N_data-n_data:N_data,:);
-
-    if extras.verbose		# Diagnostics
-##      printf("y and y_0\n");
-##      [y,y_0]
-    endif
-    
+    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);