nwidfullSLS function

Identify a full directed network using the Sequential Least Squares (SLS) algorithm.

Syntax

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

Input arguments

• data

  nwdata object

  Network data object. All input excitation, input node and output node signals (as referred to by their labels) of the network structure in the predictor model must be present in the data.

• model

  nwmodel object

  Initialized network model object. The properties of the predictor mappings G, T and H are used in the algorithm, with some restrictions:

  • The algorithm currently only supports Variable = z^-1, hence only discrete-time identification is possible.
  • Delays cannot be set; all G modules are assumed strictly proper.
  • Orders cannot be set for elements separately; a global order is used.

• order

  positive integer

  Order of estimated transfer functions in G, T and H.

• options

  nwidfullSLSOptions object

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

Output arguments

• model

  nwmodel object

  Estimated network model object. The G, T and H fields are populated with the estimated and fixed parameters. The NoiseCovariance property stores the estimated noise covariance.Details of the estimation are stored in the Report property.