User`s guide

3 Linear Model Identification
Example – Estimating Process Models with Fixed Parameters
at the Command Line
When you know the values of certain parameters in the model and want
to estimate only the values you do not know , you must specify the fixed
parameters after creating the
idproc model object.
Use the following commands to prepare the data and construct a process
model with one pole and a delay:
% Load sample data
load co2data
% Sampling interval is 0.5 min (known)
Ts = 0.5;
% Split data set into estimation data ze
% and validation data zv
ze = iddata(Output_exp1,Input_exp1,Ts,...
'TimeUnit','min');
zv = iddata(Output_exp2,Input_exp2,Ts,...
'TimeUnit','min');
mod=idproc('P1D')
MATLAB co mputes the following outpu t:
Process model with transfer f unction
K
G(s) = ---------- * exp(-Td*s)
1+Tp1*s
with K = NaN
Tp1 = NaN
Td = NaN
This model was not estimated from data.
The model parameters K, Tp1,andTd are assigned NaN values, which means
that the parameters have not yet been estimated from the data.
All process-m odel parameters are structures with the following fields:
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