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Loss functions for sets of ARX model structures

`v = ivstruc(ze,zv,NN)v = ivstruc(ze,zv,NN,p,maxsize)`

`v = ivstruc(ze,zv,NN)`

`v = ivstruc(ze,zv,NN,p,maxsize)`

`NN` is a matrix that defines a number of different
structures of the ARX type. Each row of `NN` is of
the form

nn = [na nb nk]

with the same interpretation as described for `arx`.
See `struc` for easy generation
of typical `NN` matrices.

`ze` and `zv` are `iddata` objects
containing output-input data. Only time-domain data is supported.
Models for each model structure defined in `NN` are
estimated using the instrumental variable (IV) method on data set `ze`.
The estimated models are simulated using the inputs from data set `zv`.
The normalized quadratic fit between the simulated output and the
measured output in `zv` is formed and returned in `v`.
The rows below the first row in `v` are the transpose
of `NN`, and the last row contains the logarithms
of the condition numbers of the IV matrix

A large condition number indicates that the structure is of
unnecessarily high order (see Ljung, L. *System Identification:
Theory for the User*, Upper Saddle River, NJ, Prentice-Hal
PTR, 1999, p. 498).

The information in `v` is best analyzed using `selstruc`.

If `p` is equal to zero, the computation of
condition numbers is suppressed.

The routine is for single-output systems only.

Compare the effect of different orders and delays, using the same data set for both the estimation and validation.

load iddata1 z1; v = ivstruc(z1,z1,struc(1:3,1:2,2:4)); nn = selstruc(v) m = iv4(z1,nn);

Ljung, L. *System Identification: Theory for the User*,
Upper Saddle River, NJ, Prentice-Hal PTR, 1999.

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