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estimate coefficients of linear and nonlinear differential, difference and state-space equations

functions

	estimate ode parameters of linear grey-box model
	estimate nonlinear grey-box model parameters
	linear ode (grey-box model) with identifiable parameters
	nonlinear grey-box model
	prediction error minimization for refining linear and nonlinear models

	estimate initial states of model
	set or randomize initial parameter values
	values of `idnlgrey` model initial states
	set initial states of `idnlgrey` model object
	parameter values and properties of `idnlgrey` model parameters
	set initial parameter values of `idnlgrey` model object
	obtain model parameters and associated uncertainty data
	modify values of model parameters
	simulate response of identified model

how to define and estimate linear grey-box models at the command line.
this example shows how to estimate the heat conductivity and the heat-transfer coefficient of a continuous-time grey-box model for a heated-rod system.
this example shows how to create a single-input and single-output grey-box model structure when you know the variance of the measurement noise.
estimate model parameters using linear and nonlinear grey-box modeling.
this example shows how to estimate a model that is parameterized by poles, zeros, and gains.
how to define and estimate nonlinear grey-box models at the command line.
this example shows how to write ode files for nonlinear grey-box models as matlab® and c mex files.
structured parameterization lets you exclude specific parameters from estimation by setting these parameters to specific values.
this example shows how to estimate parameters in user-defined model structures.

types of supported grey-box models.
types of supported data for estimating grey-box models.
difference between idgrey and idnlgrey model objects for representing grey-box model objects.
an identified linear model is used to simulate and predict system outputs for given input and noise signals.
configure the loss function that is minimized during parameter estimation. after estimation, use model quality metrics to assess the quality of identified models.
the estimation report contains information about the results and options used for a model estimation.
regularization is the technique for specifying constraints on the flexibility of a model, thereby reducing uncertainty in the estimated parameter values.