21,091 research outputs found
Bayesian topology identification of linear dynamic networks
In networks of dynamic systems, one challenge is to identify the
interconnection structure on the basis of measured signals. Inspired by a
Bayesian approach in [1], in this paper, we explore a Bayesian model selection
method for identifying the connectivity of networks of transfer functions,
without the need to estimate the dynamics. The algorithm employs a Bayesian
measure and a forward-backward search algorithm. To obtain the Bayesian
measure, the impulse responses of network modules are modeled as Gaussian
processes and the hyperparameters are estimated by marginal likelihood
maximization using the expectation-maximization algorithm. Numerical results
demonstrate the effectiveness of this method
Spectral identification of networks using sparse measurements
We propose a new method to recover global information about a network of
interconnected dynamical systems based on observations made at a small number
(possibly one) of its nodes. In contrast to classical identification of full
graph topology, we focus on the identification of the spectral graph-theoretic
properties of the network, a framework that we call spectral network
identification.
The main theoretical results connect the spectral properties of the network
to the spectral properties of the dynamics, which are well-defined in the
context of the so-called Koopman operator and can be extracted from data
through the Dynamic Mode Decomposition algorithm. These results are obtained
for networks of diffusively-coupled units that admit a stable equilibrium
state. For large networks, a statistical approach is considered, which focuses
on spectral moments of the network and is well-suited to the case of
heterogeneous populations.
Our framework provides efficient numerical methods to infer global
information on the network from sparse local measurements at a few nodes.
Numerical simulations show for instance the possibility of detecting the mean
number of connections or the addition of a new vertex using measurements made
at one single node, that need not be representative of the other nodes'
properties.Comment: 3
Data based identification and prediction of nonlinear and complex dynamical systems
We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin
- …