oe (System Identification Toolbox)

Estimate the parameters of an Output-Error model.

th = oe(z,nn)
th = oe(z,nn,'trace')
[th, iter_info] = oe(z,nn,maxiter,tol,lim,maxsize,T,'trace')

Description

The parameters of the Output-Error model structure

are estimated using a prediction error method.

Matrix z contains the output-input data z = [y u] where y and u are column vectors. nn can be given either as

 nn = [nb nf nk]

or as

nn = thi

In the former case, nb, and nf are the orders of the Output-Error model and nk is the delay. In the latter case, this is an initial value for the estimate, given in theta format. See "The System Identification Problem" on page 3-8 in the User's Guide for an exact definition of the orders and delay.

th is returned with the resulting parameter estimates and estimated covariances, stored in theta format.

The optional variables iter_info, lim, maxiter, maxsize, tol, and T are explained under auxvar.

For multi-input models, nb, nf, and nk are row vectors, such that entry number i gives the orders and delays associated with the i-th input.

oe does not support multi-output models. Use state-space model for this case (see canstart, n4sid, and pem)

If a last argument 'trace' is supplied, information about the progress of the iterative search for the model will be furnished to the MATLAB command window.

Algorithm

oe uses essentially the same algorithm as armax with modifications to the computation of prediction errors and gradients.