Main

## Main.ModelIdentification History

Changed lines 17-20 from:

Python)]]
to:

Changed lines 19-20 from:
to:

April 13, 2016, at 02:04 PM by 45.56.3.173 -

!!!! References

* Nikbakhsh, S., Hedengren, J.D., Darby, M., Udy, J., Constrained Model Identification Using Open-Equation Nonlinear Optimization, AIChE Spring Meeting, Houston, TX, April 2016. [[Attach:AIChE_2016_Model_ID.pdf|Presentation]] [[https://aiche.confex.com/aiche/s16/webprogram/Paper444955.html|Abstract]]

Changed line 27 from:
to:
Changed lines 27-29 from:
In class exercise
to:

Attach:dynamic_MIMO_solution.png
Changed line 15 from:
The following data set was generated in class from two voltage inputs (u) that changed the temperature (x) of two thermisters.
to:
The following data set was generated in class from two voltage inputs (u) that changed the temperature (x) of two thermistors  from the [[Main/ArduinoLab|Arduino Lab]].
Changed lines 17-18 from:
to:
Changed lines 20-21 from:
-> Attach:dynamic_MIMO_id.zip
to:
Attach:dynamic_MIMO_data.png
Changed lines 18-20 from:
-> Attach:dynamic_MIMO_data.png

Determine a linear
dynamic model that best describes the input to output relationship between voltage and temperature. Compute the steady state gain for each input to output relationship.
to:
-> Attach:dynamic_MIMO_id.zip

Determine a linear dynamic model that best describes
the input to output relationship between voltage and temperature. Compute the steady state gain for each input to output relationship. Data and example script files are posted above in Excel and MATLAB.
(:title MIMO Model Identification:)
(:keywords dynamic data, model identification, validation, differential, algebraic, tutorial, Simulink:)
(:description Multiple input, multiple output model identification for dynamic and empirical identification with an example exercise in Excel, MATLAB, and Python.:)

Multiple Input, Multiple Output (MIMO) systems can be empirically described by several linear dynamic system models. MIMO systems are more complicated than [[Main/DataSimulation|Single Input, Single Output (SISO)]] systems because of several factors including multivariate interaction, potential co-linearity of inputs, and large data processing requirements. Some common MIMO model forms include:

* Discrete transfer functions (z or q - Time series)
* Continuous transfer functions (s - Laplace variables)
* State space (A,B,C,D - time domain, linear differential equations)

Model identification in these forms typically involves fitting unknown coefficients in the model followed by an analysis to determine how many parameters are statistically significant.

!!!! Exercise

The following data set was generated in class from two voltage inputs (u) that changed the temperature (x) of two thermisters.