24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
|
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
|
+
+
+
+
-
+
+
-
-
+
+
-
+
-
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
|
######################################
###############################################################
## Version control history
###############################################################
## $Id$
## $Log$
## Revision 1.2 2001/04/04 08:36:25 gawthrop
## Restuctured to be more logical.
## Data is now in columns to be compatible with MTT.
##
## 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)
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
i_t = free(:,1); # Parameters
i_s = free(:,2)'; # Sensitivities
if nargin<9
if nargin<8
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;
reduction = inf;
predicted_reduction = 0;
par = par_0;
Par = par_0;
step = ones(n_th,1);
Error = [];
Y = [];
iterations = 0;
v = extras.v; # Levenverg-Marquardt parameter.
r = 1; # Step ratio
if extras.verbose # Diagnostics
printf("Iteration: %i\n", iterations);
printf(" error: %g\n", error);
printf(" reduction: %g\n", reduction);
printf(" prediction: %g\n", predicted_reduction);
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
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
|
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
|
112
113
114
115
116
117
118
119
120
121
122
123
124
125
|
-
-
-
-
-
-
-
-
-
-
-
-
-
-
|
r = predicted_reduction/reduction;
if (r<0.25)||(reduction<0)
v = 4*v;
elseif r>0.75
v = v/2;
endif
if extras.verbose
printf("Iteration: %i\n", iterations);
printf(" error: %g\n", error);
printf(" reduction: %g\n", reduction);
printf(" prediction: %g\n", predicted_reduction);
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
|
127
128
129
130
131
132
133
134
135
136
|
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
|
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
|
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
if extras.verbose # Diagnostics
printf("Iteration: %i\n", iterations);
printf(" error: %g\n", error);
printf(" reduction: %g\n", reduction);
printf(" prediction: %g\n", predicted_reduction);
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
endwhile
endfunction
|