Topology identification of a sparse dynamic network

Seneviratne, Akila J.; Solo, Victor

doi:10.1109/cdc.2012.6425980

Cited by 20 publications

(9 citation statements)

References 38 publications

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“…Obtain estimates G jk (q,θ N ), k ∈ D j and F jk (q,θ N ), k ∈ P j by minimizing the sum of squared prediction errors (6).…”

Section: Obtain Estimatesŵmentioning

confidence: 99%

“…Several methods have appeared that automate Granger's method for detection of causal relations by using regularization terms to set certain links in the network to zero. For instance, [6], [7] directly implement an 0 norm, whereas [8] uses the LASSO ( [9]), and [10] uses a compressed sensing approach. In [11] a Bayesian approach for topology detection is presented.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identification of Dynamic Models in Complex Networks With Prediction Error Methods: Predictor Input Selection

Dankers

Hof

Bombois

et al. 2016

IEEE Trans. Automat. Contr.

106

146

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International audienceThis paper addresses the problem of obtaining an estimate of a particular module of interest that is embedded in a dynamic network with known interconnection structure. In this paper it is shown that there is considerable freedom as to which variables can be included as inputs to the predictor, while still obtaining consistent estimates of the particular module of interest. This freedom is encoded into sufficient conditions on the set of predictor inputs that allow for consistent identification of the module. The conditions can be used to design a sensor placement scheme, or to determine whether it is possible to obtain consistent estimates while refraining from measuring particular variables in the network. As identification methods the Direct and Two Stage Prediction-Error methods are considered. Algorithms are presented for checking the conditions using tools from graph theory

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“…Obtain estimates G jk (q,θ N ), k ∈ D j and F jk (q,θ N ), k ∈ P j by minimizing the sum of squared prediction errors (6).…”

Section: Obtain Estimatesŵmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of Dynamic Models in Complex Networks With Prediction Error Methods: Predictor Input Selection

Dankers

Hof

Bombois

et al. 2016

IEEE Trans. Automat. Contr.

106

146

View full text Add to dashboard Cite

show abstract

“…The Least Absolute Shrinkage and Selection Operator (LASSO) have also been used in [21,22] with an 1 penalty to ensure a sparse solution (the 1 norm is the sum of the absolute value of the entries of the vector and serves as a surrogate to minimize the number of non-zeros elements and get a sparse solution).…”

Section: Introductionmentioning

confidence: 99%

A microscopic-view Infection model based on linear systems

Hao

Silvestre

2020

Information Sciences

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Understanding the behavior of an infection network is typically addressed from either a microscopic or a macroscopic point-of-view. The trade-off is between following the individual states at some added complexity cost or looking at the ratio of infected nodes. In this paper, we focus on developing an alternative approach based on dynamical linear systems that combines the fine information of the microscopic view without the associated added complexity. Attention is shifted towards the problems of source localization and network topology discovery in the context of infection networks where a subset of the nodes is elected as observers. Finally, the possibility to control such networks is also investigated. Simulations illustrate the conclusions of the paper with particular interest on the relationship of the aforementioned problems with the topology of the network and the selected observer/controller nodes.

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“…In Caines & Chan (1975) Anderson (1981) it is shown that it is possible to distinguish between open and closed-loop data generating systems. The reasoning is extended to more complex interconnection structures using a non-parametric approach (Materassi & Innocenti, 2010;Materassi & Salapaka, 2012); using a Bayesian approach (Chuiso & Pillonetto, 2012); and using a parametric approach supplemented by ℓ 0 regularization (Seneviratne & Solo, 2012;Yuan et al, 2011), ℓ 1 regularization (Friedman et al, 2010), and compressed sensing (Sanandaji et al, 2012). In these papers it is assumed that each node in the network is driven by an unknown, independent stochastic process, each variable is measured without sensor noise, and every variable in the network is measured.…”

Section: Introductionmentioning

confidence: 99%

Errors-in-variables identification in dynamic networks — Consistency results for an instrumental variable approach

et al. 2015

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In this paper we consider the identification of a linear module that is embedded in a dynamic network using noisy measurements of the internal variables of the network. This is an extension of the errors-in-variables (EIV) identification framework to the case of dynamic networks. The consequence of measuring the variables with sensor noise is that some prediction error identification methods no longer result in consistent estimates. The method developed in this paper is based on a combination of the instrumental variable philosophy and closed-loop prediction error identification methods, and leads to consistent estimates of modules in a dynamic network. We consider a flexible choice of which internal variables need to be measured in order to identify the module of interest. This allows for a flexible sensor placement scheme. We also present a method that can be used to validate the identified model.

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Topology identification of a sparse dynamic network

Cited by 20 publications

References 38 publications

Identification of Dynamic Models in Complex Networks With Prediction Error Methods: Predictor Input Selection

Identification of Dynamic Models in Complex Networks With Prediction Error Methods: Predictor Input Selection

A microscopic-view Infection model based on linear systems

Errors-in-variables identification in dynamic networks — Consistency results for an instrumental variable approach

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