Particle filtering of dynamical networks: highlighting observability issues
| dc.creator | Arthur Noronha Montanari | |
| dc.creator | Luis Antonio Aguirre | |
| dc.date.accessioned | 2025-05-15T14:31:40Z | |
| dc.date.accessioned | 2025-09-09T01:03:16Z | |
| dc.date.available | 2025-05-15T14:31:40Z | |
| dc.date.issued | 2019 | |
| dc.identifier.doi | https://doi.org/10.1063/1.5085321 | |
| dc.identifier.issn | 1054-1500 | |
| dc.identifier.uri | https://hdl.handle.net/1843/82304 | |
| dc.language | eng | |
| dc.publisher | Universidade Federal de Minas Gerais | |
| dc.relation.ispartof | Chaos | |
| dc.rights | Acesso Restrito | |
| dc.subject | Sistemas dinâmicos | |
| dc.subject.other | Nonlinear systems, Dynamical systems, Kuramoto models, Chaotic systems, Oscillators, Numerical methods, Graph theory, Monte Carlo methods | |
| dc.subject.other | The high-dimensionality of network systems makes it unfeasible to measure every single node. Physically, it is not (yet) possible to measure every single neuron of 100*10^9 neurons present in a brain network. Likewise, it is not of economic interest to place a phasor measurement unit in every single electrical substation of a power system. Indeed, the problem of observability is recurrent in many network systems. Thus, it is only natural that in the past ten years, the literature has been flooded by innumerable methods that provide a minimum selection of sensor nodes that claim to be sufficient and/or necessary to render a network observable. However, a framework that allows one to compare the effectiveness and practicability of these methods is still missing. Moreover, the problem becomes intrinsically more complex when dealing with dynamical networks—that is, networks whose nodes are individual dynamical systems. In this paper, we develop a framework based on Bayesian filtering as a way to assess the degree of observability conveyed by a given set of sensor nodes. This allows a further investigation of the interplay between observability, nodal dynamics, and topology on a network system, as well as a means of comparison of different methods provided in the literature. | |
| dc.title | Particle filtering of dynamical networks: highlighting observability issues | |
| dc.type | Artigo de periódico | |
| local.citation.issue | 3 | |
| local.citation.spage | 033118 | |
| local.citation.volume | 29 | |
| local.description.resumo | In a network of high-dimensionality, it is not feasible to measure every single node. Thus, an important goal is to define the optimal choice of sensor nodes that provides a reliable state reconstruction of the network system state-space. This is an observability problem. In this paper, we propose a particle filtering (PF) framework as a way to assess observability properties of a dynamical network, where each node is composed of an individual dynamical system. The PF framework is applied to two benchmarks, networks of Kuramoto and Rössler oscillators, to investigate how the interplay between dynamics and topology impacts the network observability. Based on the numerical results, we conjecture that, when the network nodal dynamics are heterogeneous, better observability is conveyed for sets of sensor nodes that share some dynamical affinity to its neighbourhood. Moreover, we also investigate how the choice of an internal measured variable of a multidimensional sensor node affects the PF performance. The PF framework effectiveness as an observability measure is compared with a well-consolidated nonlinear observability metric for a small network case and some chaotic system benchmarks. | |
| local.publisher.country | Brasil | |
| local.publisher.department | ENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICA | |
| local.publisher.initials | UFMG | |
| local.url.externa | https://pubs.aip.org/aip/cha/article/29/3/033118/567255/Particle-filtering-of-dynamical-networks |
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