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The system output in (1) can be considered as a convolution of the input and the impulse-response of the system, represented as (2).
The asymptotic stability of the system output in the presence of actuator failures is rigidly proved through standard Lyapunov approach, while the other signals of the closed-loop system are guaranteed to be bounded.
It uses explicitly a model of the system for the prediction of the future system output, in order to calculate the optimal control input which will bring the system to a required setpoint.
Furthermore, design of experiments has to comply with constraints on the system inputs and the system output, in order to prevent damage to the real system and to provide stable operational conditions during the experiment.
The system output in our model is the number of activated G-protein molecules, G a (t).
Based on these experiments, we run the discrete event simulation to generate in silico results which capture the system output in time.
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Fourth, sensitivity analysis of system outputs in relation to inputs can be informative.
Koopman operator is a linear but an infinite-dimensional operator that governs the time evolution of system outputs in a linear fashion.
We identify common design principals that highlight how the spatial organization of signal transduction circuits can be used as a fundamental control mechanism to modulate system outputs in vivo.
One way to address the multiple solution issue is adding more system outputs in the similarity analysis.
Results show that the system output error in comparison to the historical data is less than 5%, and it can resemble the council's behavior.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com