CD Construct Noise Model VI

Owning Palette: Stochastic Systems VIs

Requires: Control Design and Simulation Module

Constructs a first- and second-order statistical noise model. A noise model defines the statistical behavior of the process noise vector w and the measurement noise vector v. Wire data to the E{w}, E{v}, Q, R, or N inputs to determine the polymorphic instance to use or manually select the instance.

Note  If you do not wire a value to the E{w}, E{v}, Q, R, N, or symbolic parameters, this VI replaces the unwired parameters with an appropriately-sized vector or matrix of zeros.

This VI uses the CD Verify Noise Model VI to verify that the dimensions of the noise model are consistent with the Stochastic State-Space Model you provide.

CD Construct Noise Model (Numeric)

	Stochastic State-Space Model specifies a mathematical representation of a stochastic system.
	E{w} specifies the mean vector of the process noise vector w.
	E{v} specifies the mean vector of the measurement noise vector v.
	Q specifies the auto-covariance matrix of the process noise vector.
	R specifies the auto-covariance matrix of the measurement noise vector.
	error in describes error conditions that occur before this node runs. This input provides standard error in functionality.
	N specifies the cross-covariance matrix between the process noise and the measurement noise vectors. Note  If the process noise and measurement noise vectors are uncorrelated, either specify a matrix of zeros for this parameter or do not wire a value to this parameter.
	Second-Order Statistics Noise Model Out returns a mathematical representation of the noise model of the Stochastic State-Space Model.
	error out contains error information. This output provides standard error out functionality.

CD Construct Noise Model (Symbolic)

	Stochastic State-Space Model specifies a mathematical representation of a stochastic system.
	Symbolic E{w} specifies the symbolic representation of the mean vector of the process noise vector.
	Symbolic E{v} specifies the symbolic representation of the mean vector of the measurement noise vector.
	Symbolic Q specifies the symbolic representation of the auto-covariance matrix of the process noise vector.
	Symbolic R specifies the symbolic representation of the auto-covariance matrix of the measurement noise vector.
	error in describes error conditions that occur before this node runs. This input provides standard error in functionality.
	Symbolic N specifies the symbolic representation of the cross-covariance matrix between the process noise and measurement noise vectors.
	Variables contains the name and value of each variable. Name is a variable name this VI uses to define the data of the system model. Variable names can be a combination of letters and numbers. A variable name that begins with a capital letter E can produce unpredictable errors if parts of the original string represent numbers like 1E–2. Avoid terms beginning with E in such cases. Value is the numeric value this VI associates with the variable. The VI uses this value to evaluate the model.
	Second-Order Statistics Noise Model Out returns a mathematical representation of the noise model of the Stochastic State-Space Model.
	error out contains error information. This output provides standard error out functionality.