This paper formulates and solves the network reconstruction problem for linear time-
invariant systems. The problem is motivated from a variety of disciplines, but it has recently …

A recurrent neural network is proposed for solving the non-smooth convex optimization
problem with the convex inequality and linear equality constraints. Since the objective …

We present a powerful experimental-computational technology for inferring network models
that predict the response of cells to perturbations, and that may be useful in the design of …

Like engineering systems, biological systems must also operate effectively in the presence
of internal and external uncertainty—such as genetic mutations or temperature changes, for …

Genes play a central role in all biological processes. DNA microarray technology has made
it possible to study the expression behavior of thousands of genes in one go. Often, gene …

We address the problem of identifying the topology of an unknown weighted, directed
network of LTI systems stimulated by wide-sense stationary noises of unknown power …

This paper considers the topology identification issue for fractional complex networks
(FCNs) based on the synchronization in finite-time. Firstly, two lemmas with respect to the …

This paper considers the state estimation problem for Markovian jum** genetic regulatory
networks (GRNs) with mode-dependent leakage and time-varying delays. In order to …

This paper addresses the problem of identifying the graph structure of a dynamical network
using measured input/output data. This problem is known as topology identification and has …

We consider a blind identification problem in which we aim to recover a statistical model of a
network without knowledge of the network's edges but based solely on nodal observations …