Contents
Load my model
dataModel = loadModel('OneStep');
Simulatate our "real model"
we use the nominal model to generate some data
exp1 = constructExperiment(30*60, 10); data = simulate(dataModel, exp1);
Scramble the parameters onthe model
m = dataModel; m.p = m.p.*random('log',0,1,size(dataModel.p)); m = postProcessModel(m); beforeFit = simulate(m, exp1); tData = [0, logspace(0, log10(29*60), 10)]; myData.y = data.y(tData); myData.t = tData; % Plot the data and the model before the fit on the same axis figure(1),clf plot(myData.t, myData.y, 'o'); hold on; drawnow; plot(beforeFit.t, beforeFit.y(beforeFit.t), '--'); hold off; disp('Hit any Key to continue'); pause;
Hit any Key to continue

Fit the new model to data generated from the old model
figure(2),clf obj = constructObjectiveOutputSSE(m, 1:m.nY, myData); [mFit,G] = fitParameters(m, obj, exp1); afterFit = simulate(mFit, exp1);
max Line search Directional First-order Iter F-count f(x) constraint steplength derivative optimality Procedure 0 1 1.98765 -0.008716 1 3 0.758514 -0.01118 0.001 -1.1e+003 500 2 5 0.118325 -0.01096 0.0202 -12.3 671 3 7 0.0151471 -0.02127 0.116 0.208 17.1 4 13 0.0148912 -0.0226 0.0115 0.0277 9.28 5 17 0.00107799 -0.02495 0.0149 0.552 2.02 6 19 0.000161612 -0.02434 1 -0.000466 0.625 7 21 4.07612e-005 -0.02388 1 3.21e-005 0.101 Hessian modified 8 23 3.88479e-005 -0.02391 1 4.83e-008 0.00492 Hessian modified Optimization terminated: magnitude of directional derivative in search direction less than 2*options.TolFun and maximum constraint violation is less than options.TolCon. No active inequalities.

Plot the results
figure(3),clf plot(myData.t, myData.y, 'o'); hold on; plot(beforeFit.t, beforeFit.y(beforeFit.t), '--'); plot(afterFit.t, afterFit.y(afterFit.t), '-'); hold off;
