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In the first step a neural state-space model is transformed into a linear fractional transformation (LFT) representation to obtain a discrete-time quasi-LPV model of a nonlinear plant from input-output data only.
Here a discrete-time polytopic quasi linear parameter varying (LPV) model of a nonlinear system based on a neural state-space model is proposed, whereas in the joint paper (Abbas and Werner [2008]) a neural state-space model is transformed into a linear fractional transformation (LFT) representation to obtain a discrete-time quasi-LPV model of the nonlinear system.
Here a neural state-space model is transformed into a linear fractional transformation (LFT) representation to obtain a discrete-time quasi-linear parameter-varying (LPV) model of a nonlinear plant, whereas in the joint paper (Abbas and Werner [2008]) a method is proposed to transform the neural state-space into a discrete-time polytopic quasi-LPV model.
Step 2: The ODE model is transformed using the proposed linear transformation pattern.
This paper demonstrates a transformation practice where a Simulink model is transformed into a Function Block model in FBDK.
The rational model is transformed to a time domain model using inverse Laplace transformation.
The longitudinal model is transformed into the strict feedback formation.
The identified model is transformed into modal realization.
Then, the reference model is transformed to the model shown in Fig. 8 (c).
Second, the estimators model is transformed into a model compliant with a power estimation metamodel.
For more complex network models, the probability model is transformed into a normal distribution function.
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