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title: Background
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System modeling can be categorized into three broad categories:
## White-box models
In white-box systems, the internal dynamics are evident. The system can be understood in terms of inputs, outputs, and exact internal dynamics. A white-box model assumes perfect knowledge of the system.
## Grey-box models
Grey-box models add some obfuscation to the system dynamics. While the structure or mathematical form of the dynamics may be known, the exact parameter values are not fullt evident. Grey-box models are constrained by the *apriori* knowledge of the system and estimate parameters within those bounds.
An example could be a simple RC circuit with unknown capacitance and resistance. The equations governing the current through the components are known. A physical model can be constructed based off of those equations. The model, in combination with actual measurements from the circuit then estimates the values of parameters in the equations such that the difference between the model's predictions and the actual readings is minimized.
## Black-box models
A Black-box model foregoes any *apriori* knowledge about the distribution of system parameters. Instead it learns the mechanics from scratch. A neural network used to approximate an RC circuit is a black-box model. The network simply learns the mappings from the inputs to the outputs.
### Further reading:
* [System Identification][1]
* [Grey box model][2]
[1]: https://en.wikipedia.org/wiki/System_identification
[2]: https://en.wikipedia.org/wiki/Grey_box_model