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Method Article
Sean P. Cornelius
The Motter Group
Corresponding Author
Adilson E. Motter
The Motter Group
Corresponding Author
License:
This work is licensed under a CC BY-NC 3.0 License. Read Full License
We present an algorithm for the control of complex networks and other nonlinear, high-dimensional dynamical systems. The computational approach is based on the recently-introduced concept of compensatory perturbations—intentional alterations to the state of a complex system that can drive it to a desired target state even when there are constraints on the perturbations that forbid reaching the target state directly. Included here is ready-to-use software that can be applied to identify eligible control interventions in a general system described by coupled ordinary differential equations, whose specific form can be specified by the user. The algorithm is highly scalable, with the computational cost scaling as the number of dynamical variables to the power 2.5.
Keywords
control, complex networks, nonlinearity, algorithm, software
Figure 1
The authors declare no competing financial interests.
This is a list of supplementary files associated with this preprint. Click to download.
- supplement0.zip
Supplementary File 1 Python source code for NECO ZIP archive containing the source code for NECO (neco.py) and an example control problem (neco_example.py).
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Associated Publications
Realistic control of network dynamics
by Sean P. Cornelius, William L. Kath, and Adilson E. Motter
Nature Communications (25 June, 2013)
An open repository of community-contributed protocols sponsored by Nature Research.
Learn more about Protocol Exchange
Method Article
Sean P. Cornelius
The Motter Group
Corresponding Author
Adilson E. Motter
The Motter Group
Corresponding Author
Version History
CURRENT STATUS: Posted
Version 1
Posted 27 Jun, 2013
No community comments so far
Metrics
Comments: 0
HTML Views: ...
License:
This work is licensed under a CC BY-NC 3.0 License. Read Full License
We present an algorithm for the control of complex networks and other nonlinear, high-dimensional dynamical systems. The computational approach is based on the recently-introduced concept of compensatory perturbations—intentional alterations to the state of a complex system that can drive it to a desired target state even when there are constraints on the perturbations that forbid reaching the target state directly. Included here is ready-to-use software that can be applied to identify eligible control interventions in a general system described by coupled ordinary differential equations, whose specific form can be specified by the user. The algorithm is highly scalable, with the computational cost scaling as the number of dynamical variables to the power 2.5.
Figure 1
The authors declare no competing financial interests.
This is a list of supplementary files associated with this preprint. Click to download.
- supplement0.zip
Supplementary File 1 Python source code for NECO ZIP archive containing the source code for NECO (neco.py) and an example control problem (neco_example.py).
Loading...
Associated Publications
Realistic control of network dynamics
by Sean P. Cornelius, William L. Kath, and Adilson E. Motter
Nature Communications (25 June, 2013)