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Diffstat (limited to 'tests/general_tests/lsqnonlin/lsqnonlin_param2.sce')
| -rw-r--r-- | tests/general_tests/lsqnonlin/lsqnonlin_param2.sce | 31 |
1 files changed, 31 insertions, 0 deletions
diff --git a/tests/general_tests/lsqnonlin/lsqnonlin_param2.sce b/tests/general_tests/lsqnonlin/lsqnonlin_param2.sce new file mode 100644 index 0000000..659294a --- /dev/null +++ b/tests/general_tests/lsqnonlin/lsqnonlin_param2.sce @@ -0,0 +1,31 @@ +// Check for the params + +function y=yth(t, x) +y = x(1)*exp(-x(2)*t) +endfunction +// we have the m measures (ti, yi): +m = 10; +tm = [0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5]'; +ym = [0.79, 0.59, 0.47, 0.36, 0.29, 0.23, 0.17, 0.15, 0.12, 0.08]'; +// measure weights (here all equal to 1...) +wm = ones(m,1); +// and we want to find the parameters x such that the model fits the given +// data in the least square sense: +// +// minimize f(x) = sum_i wm(i)^2 ( yth(tm(i),x) - ym(i) )^2 +// initial parameters guess +x0 = [1.5; 0.8]; +// in the first examples, we define the function fun and dfun +// in scilab language +function y=myfun(x, tm, ym, wm) +y = wm.*( yth(tm, x) - ym ) +endfunction + +options = list("Maxiter") + +//Error +//lsqnonlin: Size of parameters should be even +//at line 190 of function lsqnonlin called by : +//[xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin(myfun,x0,[],[],options) + +[xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin(myfun,x0,[],[],options) |