Github Baggiogi Data_driven_control

Baggiogi (Giacomo Baggio) · GitHub
Baggiogi (Giacomo Baggio) · GitHub

Baggiogi (Giacomo Baggio) · GitHub The folder synthetic contains the matlab functions and scripts for computing optimal data driven controls and for comparing the performance of model based and data driven controls in synthetic experimental settings. In this paper we overcome this limitation, and develop a data driven framework to control a complex dynamical network optimally and without requiring any knowledge of the network dynamics.

GitHub - Baggiogi/data_driven_control
GitHub - Baggiogi/data_driven_control

GitHub - Baggiogi/data_driven_control From model based to data driven control of network dynamics giacomo baggio department of information engineering (dei) university of padova october 14, 2020 biology. In this paper we overcome this limitation, and develop a data driven framework to control a complex network optimally and without any knowledge of the network dynamics. Controlling the behavior of a complex network usually requires a knowledge of the network dynamics. baggio et al. propose a data driven framework to control a complex dynamical network, effective for non complete or random datasets, which is of relevance for power grids and neural networks. The folder synthetic contains the matlab functions and scripts for computing optimal data driven controls and for comparing the performance of model based and data driven controls in synthetic experimental settings.

Data-Driven-Control · GitHub
Data-Driven-Control · GitHub

Data-Driven-Control · GitHub Controlling the behavior of a complex network usually requires a knowledge of the network dynamics. baggio et al. propose a data driven framework to control a complex dynamical network, effective for non complete or random datasets, which is of relevance for power grids and neural networks. The folder synthetic contains the matlab functions and scripts for computing optimal data driven controls and for comparing the performance of model based and data driven controls in synthetic experimental settings. In this paper we overcome this limitation, and develop a data driven framework to control a complex dynamical net work optimally and without requiring any knowledge of the network dynamics. Contribute to baggiogi/data driven control development by creating an account on github. Contribute to baggiogi/data driven control development by creating an account on github. Contribute to baggiogi/data driven control development by creating an account on github.

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