Predicting and Controlling Complex Networks
Abstract
The principal Objective of the project was to develop methods to predict and control complex networks. For prediction, a number of methods were articulated and tested to uncover the structures and topologies of complex networks as well as various dynamical processes on the networks based solely on time series data or measured signals. A compressive sensing based framework for network and nonlinear dynamical systems reconstruction was pioneered. For control, key issues including linear controllability of complex networks, control energy, control of collective dynamics, and control of nonlinear dynamics on complex networks were addressed. A number of new phenomena in complex dynamical systems were uncovered and understood, and computational paradigms were established for prediction and control.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 22, 2015
- Accession Number
- ADA619238
Entities
People
- Ying C. Lai
Organizations
- Arizona State University