Affordable Access

Controllability Metrics on Networks with Linear Decision Process-type Interactions and Multiplicative Noise

Authors
Type
Preprint
Publication Date
Submission Date
Identifiers
arXiv ID: 1510.03157
Source
arXiv
License
Yellow
External links

Abstract

This paper aims at the study of controllability properties and induced controllability metrics on complex networks governed by a class of (discrete time) linear decision processes with mul-tiplicative noise. The dynamics are given by a couple consisting of a Markov trend and a linear decision process for which both the '' deterministic '' and the noise components rely on trend-dependent matrices. We discuss approximate, approximate null and exact null-controllability. Several examples are given to illustrate the links between these concepts and to compare our results with their continuous-time counterpart (given in [16]). We introduce a class of backward stochastic Riccati difference schemes (BSRDS) and study their solvability for particular frameworks. These BSRDS allow one to introduce Gramian-like controllability metrics. As application of these metrics, we propose a minimal intervention-targeted reduction in the study of gene networks.

Statistics

Seen <100 times