WOLFRAM SYSTEMMODELER

Variance

Calculates the empirical variance of its input signal

Wolfram Language

In[1]:=
SystemModel["Modelica.Blocks.Math.Variance"]
Out[1]:=

Information

This information is part of the Modelica Standard Library maintained by the Modelica Association.

This block calculates the empirical variance of its input signal. It is based on the formula (but implemented in a more reliable numerical way):

y = mean(  (u - mean(u))^2  )

The parameter t_eps is used to guard against division by zero (the variance computation starts at <simulation start time> + t_eps and before that time instant y = 0).

The variance of a signal is also equal to its mean power.

This block is demonstrated in the examples UniformNoiseProperties and NormalNoiseProperties.

Parameters (1)

t_eps

Value: 1e-7

Type: Time (s)

Description: Variance calculation starts at startTime + t_eps

Connectors (2)

u

Type: RealInput

Description: Noisy input signal

y

Type: RealOutput

Description: Variance of the input signal

Used in Examples (2)

UniformNoiseProperties

Modelica.Blocks.Examples.NoiseExamples

Demonstrates the computation of properties for uniformally distributed noise

NormalNoiseProperties

Modelica.Blocks.Examples.NoiseExamples

Demonstrates the computation of properties for normally distributed noise

Used in Components (1)

StandardDeviation

Modelica.Blocks.Math

Calculates the empirical standard deviation of its input signal