$LastChangedDate$datafitParameter identification based on measured dataCalling Sequence[p,err]=datafit([imp,] G [,DG],Z [,W],[contr],p0,[algo],[df0,[mem]],
[work],[stop],['in'])Parametersimpscalar argument used to set the trace mode.
imp=0 nothing (execpt errors) is reported,
imp=1 initial and final reports,
imp=2 adds a report per iteration,
imp>2 add reports on linear search. Warning,
most of these reports are written on the Scilab standard
output.Gfunction descriptor (e=G(p,z), e: ne x 1, p: np x 1, z: nz x
1)DGpartial of G wrt p function descriptor (optional; S=DG(p,z),
S: ne x np)Zmatrix [z_1,z_2,...z_n] where z_i (nz x 1) is the ith
measurementWweighting matrix of size ne x ne (optional; defaut no
ponderation)contr: 'b',binf,bsup with
binf and bsup real vectors
with same dimension as p0.
binf and bsup are lower and
upper bounds on p.p0initial guess (size np x 1)algo: 'qn' or 'gc' or
'nd' . This string stands for quasi-Newton
(default), conjugate gradient or non-differentiable respectively.
Note that 'nd' does not accept bounds on
x ).df0real scalar. Guessed decreasing of f at
first iteration. (df0=1 is the default
value).mem :integer, number of variables used to approximate the Hessian,
(algo='gc' or 'nd'). Default value is around
6.stopsequence of optional parameters controlling the convergence of
the algorithm. stop= 'ar',nap, [iter [,epsg [,epsf
[,epsx]]]]"ar"reserved keyword for stopping rule selection defined as
follows:napmaximum number of calls to fun
allowed.itermaximum number of iterations allowed.epsgthreshold on gradient norm.epsfthreshold controlling decreasing of
fepsxthreshold controlling variation of x.
This vector (possibly matrix) of same size as
x0 can be used to scale
x."in"reserved keyword for initialization of parameters used when
fun in given as a Fortran routine (see
below).pColumn vector, optimal solution founderrscalar, least square error.Descriptiondatafit is used for fitting data to a model. For
a given function G(p,z), this function finds the best
vector of parameters p for approximating
G(p,z_i)=0 for a set of measurement vectors
z_i. Vector p is found by minimizing
G(p,z_1)'WG(p,z_1)+G(p,z_2)'WG(p,z_2)+...+G(p,z_n)'WG(p,z_n)datafit is an improved version of
fit_dat.ExamplesSee Alsolsqrsolveoptimleastsq