CD Correlated Gaussian Random Noise VI

Owning Palette: Stochastic Systems VIs

Requires: Control Design and Simulation Module

Generates a sample of one or two Gaussian-distributed random vectors, which you can use as the noise vectors w and v. You specify the mean, auto-covariance, and cross-covariance of these vectors. You must manually select the polymorphic instance to use.

Use this VI to generate values for the Process Noise w(k) and Measurement Noise v(k) inputs of the CD Discrete Stochastic State-Space function.

CD Correlated Gaussian Random Noise (One Vector)

	seed determines how to generate the internal seed state. If seed is greater than 0, this VI uses seed to generate the internal state directly. If seed is less than or equal to 0, this VI uses a random number to generate the internal state. The default is –1.
	E{x} specifies the mean of the Gaussian random vector x. The length of E{x} determines the length of the random vector sample x and the dimensions of the Cov{x,x} matrix.
	Cov{x,x} specifies the covariance of the Gaussian-distributed random vector x. This covariance matrix can be either diagonal or non-diagonal, which means that samples of x can be uncorrelated or correlated with each other. If n is the length of E{x}, the Cov{x,x} must be an n × n matrix. The Cov{x,x} matrix must be symmetric and positive semi-definite such that Cov{x,x} = Cov{x,x}T ≥ 0.
	error in describes error conditions that occur before this node runs. This input provides standard error in functionality.
	random vector sample x returns a random sample of the Gaussian random vector x. The length of random vector sample x is equal to the length of the E{x} vector.
	error out contains error information. This output provides standard error out functionality.

CD Correlated Gaussian Random Noise (Two Vectors)

	seed determines how to generate the internal seed state. If seed is greater than 0, this VI uses seed to generate the internal state directly. If seed is less than or equal to 0, this VI uses a random number to generate the internal state. The default is –1.
	E{x} specifies the mean of the Gaussian random vector x. The length of E{x} determines the length of the random vector sample x and the dimensions of the Cov{x,x} matrix.
	E{y} specifies the mean of the Gaussian random vector y. The length of E{y} determines the length of the random vector sample y and the dimensions of the Cov{y,y}.
	Cov{x,x} specifies the covariance of the Gaussian-distributed random vector x. This covariance matrix can be either diagonal or non-diagonal, which means that samples of x can be uncorrelated or correlated with each other. If n is the length of E{x}, the Cov{x,x} must be an n × n matrix. The Cov{x,x} matrix must be symmetric and positive semi-definite such that Cov{x,x} = Cov{x,x}T ≥ 0.
	Cov{y,y} specifies the covariance of the Gaussian-distributed random vector y. This covariance matrix can be either diagonal or non-diagonal, which means that samples of y can be uncorrelated or correlated with each other. If m is the length of E{y}, the Cov{y,y} matrix must be an m × m matrix. The Cov{y,y} matrix must be symmetric and positive semi-definite such that Cov{y,y} = Cov{y,y}T ≥ 0.
	error in describes error conditions that occur before this node runs. This input provides standard error in functionality.
	Cov{x,y} specifies the cross-covariance between the Gaussian random vectors x and y. If n is the length of E{x} and m is the length of E{y}, the Cov{x,y} matrix must be an n × m matrix. The Cov{x,y} matrix also must satisfy the following relationship: If you specify a matrix of zeros for the Cov{x,y} matrix, samples of the random vectors x and y are uncorrelated.
	random vector sample x returns a random sample of the Gaussian random vector x. The length of random vector sample x is equal to the length of the E{x} vector.
	random vector sample y returns a random sample of the Gaussian random vector y. The length of random vector sample y is equal to the length of the E{y} vector.
	error out contains error information. This output provides standard error out functionality.