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Pré-Publication, Document De Travail Année : 2015

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

Tidiane Diallo
  • Fonction : Auteur
PS
Dan Goreac
  • Fonction : Auteur
PS

Résumé

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.
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Dates et versions

hal-01214318 , version 1 (12-10-2015)
hal-01214318 , version 2 (14-12-2016)

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Citer

Tidiane Diallo, Dan Goreac. Controllability Metrics on Networks with Linear Decision Process-type Interactions and Multiplicative Noise. 2015. ⟨hal-01214318v1⟩
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