Estimate general input-output models using recursive pseudolinear regression method
thm = rplr(z,nn,adm,adg) [thm,yhat,P,phi] = rplr(z,nn,adm,adg,th0,P0,phi0)
rplr is not compatible with MATLAB®
Coder™ or MATLAB
This is a direct alternative to
has essentially the same syntax. See
an explanation of the input and output arguments.
rplr differs from
that it uses another gradient approximation. See Section 11.5 in Ljung
(1999). Several of the special cases are well-known algorithms.
When applied to ARMAX models, (
nn = [na nb nc 0 0 nk]),
the extended least squares (ELS) method. When applied to the output-error
nn = [0 nb 0 0 nf nk]), the method is
known as HARF in the
adm = 'ff' case
and SHARF in the
adm = 'ng' case.
rplr can also be applied to the time-series
case in which an ARMA model is estimated with:
z = y nn = [na nc]
Estimate Output-Error Model Parameters Using Recursive Pseudolinear Regression
Specify the order and delays of an Output-Error model structure.
na = 0; nb = 2; nc = 0; nd = 0; nf = 2; nk = 1;
Load the estimation data.
load iddata1 z1
Estimate the parameters using forgetting factor algorithm, with forgetting factor 0.99.
EstimatedParameters = rplr(z1,[na nb nc nd nf nk],'ff',0.99);
For more information about HARF and SHARF, see Chapter 11 in Ljung (1999).