Continuous Mean

Calculates the empirical expectation (mean) value of its input signal

 Description The Continuous Mean component continuously calculates the mean value of its input signal. This can be used to determine the empirical expectation value of a random signal, such as generated by the Noise blocks. The parameter ${t}_{\mathrm{\epsilon }}$ is used to guard against division by zero (the mean value computation starts at ${t}_{0}+{t}_{\mathrm{\epsilon }}$).
 Equations $y=\left\{\begin{array}{cc}\mathrm{\mu }& {t}_{0}+{t}_{\mathrm{\epsilon }}\le t\\ u& \mathrm{otherwise}\end{array}$ $\frac{d\mathrm{\mu }}{\mathrm{dt}}=\left\{\begin{array}{cc}\frac{u-\mathrm{\mu }}{t-{t}_{0}}& {t}_{0}+{t}_{\mathrm{\epsilon }}\le t\\ 0& \mathrm{otherwise}\end{array}$

Connections

 Name Description Modelica ID $u$ Noisy input signal u $y$ Expectation (mean) value of the input signal y

Parameters

 Name Default Units Description Modelica ID ${t}_{\mathrm{\epsilon }}$ $1·{10}^{-7}$ $s$ Mean value calculation starts at ${t}_{0}+{t}_{\mathrm{\epsilon }}$ t_eps

 Modelica Standard Library The component described in this topic is from the Modelica Standard Library. To view the original documentation, which includes author and copyright information, click here.