Main Content

Nonlinear Grey-Box Models

Estimate coefficients of nonlinear differential, difference and state-space equations

If you understand the physics of your system and can represent the system using ordinary differential or difference equations (ODEs) with unknown parameters, then you can use System Identification Toolbox™ commands to perform grey-box modeling.. Grey-box model ODEs specify the mathematical structure of the model explicitly, including couplings between parameters. Grey-box modeling is useful when you know the relationships between variables, constraints on model behavior, or explicit equations representing system dynamics.

You can represent a nonlinear grey-box model using an idnlgrey object, which requires that you write a function to describe the dynamics as a set of first-order differential equations. For more information, see Estimate Nonlinear Grey-Box Models.

Functions

expand all

nlgreyestEstimate nonlinear grey-box model parameters
nlgreyestOptionsOption set for nlgreyest
idnlgreyNonlinear grey-box model
pemPrediction error minimization for refining linear and nonlinear models
generateMATLABFunctionGenerate MATLAB functions that evaluate the state and output functions, and their Jacobians, of a nonlinear grey-box or neural state-space model (Since R2022b)
initSet or randomize initial parameter values
getparParameter values and properties of idnlgrey model parameters
setparSet initial parameter values of idnlgrey model object
getpvecObtain model parameters and associated uncertainty data
setpvecModify values of model parameters
getinitValues of idnlgrey model initial states
setinitSet initial states of idnlgrey model object
findstatesEstimate initial states of model
findstatesOptionsOption set for findstates
simSimulate response of identified model
simOptionsOption set for sim

Topics

Featured Examples