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nwmodel class

Captures a model of a dynamic network, in which parameters governing the dynamic relationships between the elements of the network can be estimated. It defines the mapping of all internal nodes, excitation signals and noise sources to the internal nodes of the network (as denoted in w(t)=G(q)w(t)+R(q)r(t)+H(q)e(t)w(t) = G(q)w(t) + R(q)r(t) + H(q)e(t) ). It is used in full network identification and simulation. The nwmodel class is a special case of the network predictor model (nwpredmodel) class, in which all excitations of the network are used as input signals, and all nodes are used as input and output signals of the predictor model. All properties and methods of nwpredmodel are also present in this class.


  • model = nwmodel(NetworkStructure)  creates a full network identification model based on the topology in the provided LabelledAdjStruct argument.
  • model = nwmodel(G,T,H)  creates a parameterized full network identification model based on the G, T and H transfer functions. The underlying network structure is inferred from these objects.
  • model = nwmodel(__,Name=Value)  creates a full network identification model with additional properties set according to the provided values. The same properties can be set through this method as in nwpredmodel.


See nwpredmodel for a list of properties shared with the nwpredmodel class.

  • L

    positive scalar

    Number of nodes.

  • K

    positive scalar

    Number of excitations.

  • p

    positive scalar

    Number of noise signals.


See nwpredmodel for a list of methods shared with the nwpredmodel class.

  • simulate

    Simulate identified network model.


    • w = simulate(model,r,e,t)  simulates the full network and returns the node signals w in response to the excitations r and noise signals e over the time samples t.

    Input arguments

    • model

      nwmodel object
    • r

      [N,nR][N,n_{R}] double array

      Input excitation signals.

    • e

      [N,nE][N,n_{E}] double array

      Input noise signals.

    • t

      [N,1][N,1] double array

      Time samples.

    Output arguments

    • w

      [N,nY][N,n_{Y}] double array

      Simulated node signals