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 ). 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.
Construction
model = nwmodel(NetworkStructure)
creates a full network identification model based on the topology in the providedLabelledAdjStruct
argument.model = nwmodel(G,T,H)
creates a parameterized full network identification model based on theG
,T
andH
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 innwpredmodel
.
Properties
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.
Methods
See nwpredmodel
for a list of methods shared with the nwpredmodel
class.
simulate
Simulate identified network model.
Syntax
w = simulate(model,r,e,t)
simulates the full network and returns the node signalsw
in response to the excitationsr
and noise signalse
over the time samplest
.
Input arguments
model
nwmodel
objectr
double array Input excitation signals.
e
double array Input noise signals.
t
double array Time samples.
Output arguments
w
double array Simulated node signals