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error_old = inf;
error=1e50;
theta = theta_0;
Theta = [];
Error = [];
Y = [];
iterations = 0;
while (abs(error_old-error)>criterion)&&(iterations<max_iterations)
iterations = iterations + 1;
error_old_old = error_old;
error_old = error;
eval(sprintf("[t,y,y_theta] = \
mtt_s%s(system_name,theta,free);",method)); # Simulate system
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error_old = inf;
error=1e50;
theta = theta_0;
Theta = [];
Error = [];
Y = [];
iterations = 0;
while (abs(error_old-error)>criterion)&&(abs(error)>criterion)&&(iterations<max_iterations)
iterations = iterations + 1;
error_old_old = error_old;
error_old = error;
eval(sprintf("[t,y,y_theta] = \
mtt_s%s(system_name,theta,free);",method)); # Simulate system
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if error<(error_old+criterion) # Save some diagnostics
Error = [Error error]; # Save error
Theta = [Theta theta]; # Save parameters
Y = [Y y]; # Save output
endif
## Update the estimate if we are not done yet.
if (abs(error_old-error)>criterion)&&(iterations<max_iterations)
if error>(error_old+criterion) # Reduce step size and try again
factor = 10;
disp(sprintf("%2.2f*step",alpha));
error = error_old; # Go back to previous error value
error_old = inf; # Don't let it think its converged
theta(free) = theta(free) + step; # Reverse step
step = alpha*step; # new stepsize
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if error<(error_old+criterion) # Save some diagnostics
Error = [Error error]; # Save error
Theta = [Theta theta]; # Save parameters
Y = [Y y]; # Save output
endif
## Update the estimate if we are not done yet.
if (abs(error_old-error)>criterion)&&(abs(error)>criterion)&&(iterations<max_iterations)
if error>(error_old+criterion) # Reduce step size and try again
factor = 10;
disp(sprintf("%2.2f*step",alpha));
error = error_old; # Go back to previous error value
error_old = inf; # Don't let it think its converged
theta(free) = theta(free) + step; # Reverse step
step = alpha*step; # new stepsize
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