nwidPEM function

Identify a full network or a single target module using prediction error minimization (PEM), based on the implementation in the System Identification toolbox. The network model is transformed into a state space model to support optimization with pem.

Syntax

• model = nwidPEM(data,model,orders,options)  performs identification based on the provided data, initialized network model, model orders and option set. The identified predictor model is returned.

Input arguments

• data

  nwdata object

  Network data object. The input excitation, input node and output node signals (as referred to by their labels) in the Rset, Dset and Yset of the predictor model must be present in the data.

• model

  nwpredmodel object	nwmodel object

  Initialized network model object. Use an nwmodel object to identify the full network, and an nwpredmodel object to identify a single target module. For each element, whether or not it is fixed, the number of delays, the orders of the numerators and denominators, parameter values of fixed elements and parameter initializations can be set using the G, T and H fields (PredictorMap objects). H must specify a square matrix and is enforced to be monic by the algorithm.

• orders

  structure array

  Model orders. Can be used to override properties of the initialized network model object. Use a structure array with optional fields G, T and H, which should each be structure arrays themselves with the optional fields npnum, npden and d (to set the analogous PredictorMap properties). Use an empty structure array to use the initialized network model object without modifications.

• options

  nwidPEMOptions object

  Option set for nwidPEM function. Can be constructed using options = nwidPEMOptions for the default option set or options = nwidPEMOptions(Name=Value,...) to set specified options.

Output arguments

• model

  nwpredmodel object	nwmodel object

  Estimated network model object. This is an nwmodel object after full network identification, and an nwpredmodel object after single module identification. The npnum and npden properties of the G, T and H fields are populated with the estimated parameter values. The e properties of G, T and H contain the fixed and estimated transfer functions of the predictor model connections.