I think you can simply add a "disp" command after definition of your objfun. Because what you pass to lsqnonlin is a vector, you can calculate your function value and eventually RMSE. Such as:
function objfun = objfun(prm)
(...define prm)
objfun = Tfield_obj-Tsim_obj;
lsqnonlin_value = sum(objfun.^2);
nData = size(Tfield_obj,1);
iter = iter+1
RMSE_iter(end+1) = sqrt(lsqnonlin_value/nData);
disp(['--- Current FunEval/Iteration completed. ObjFun value: ',num2str(lsqnonlin_value),'. RMSE: ',num2str(RMSE_iter(end)),' ---'])
end
I also wanted to know how the lsqnonlin is progressing in parameters update, so at each parameter update I requested it to output the current parameter combination (updated at each function evaluation/iteration):
function objfun = objfun(prm)
varParams(step).kfrost = prm(1); % kfrost
varParams(step).Porosity = prm(2); % Porosity
varParams(step).a = prm(3); % a
varParams(step).b = prm(4); % b
disp('----------- NEW PARAMETER GUESS -----------')
disp(['Running FunEval with new parameter guess: '...
'kfrost = ',num2str(varParams(step).kfrost),' [W/m/K], '...
'Porosity = ',num2str(varParams(step).Porosity),' [m3/m3], '...
'a = ',num2str(varParams(step).a),' [-], '...
'b = ',num2str(varParams(step).b),' [-].'])
(...define objfun)
end
What lsqnonlin displays during the iterative display are best result of all function evaluations within the given iteration.
