Convergence analysis of weighted SPSA-based consensus algorithm in distributed parameter estimation problem

Anna Sergeenko, Victoria Erofeeva, Oleg Granichin, Olga Granichina, Anton Proskurnikov

Research output: Contribution to journalConference articlepeer-review

Abstract

In this paper, we study a distributed parameter estimation problem in a large-scale network of communication sensors. The goal of the sensors is to find a global estimate of an unknown parameter minimizing, which minimizes some aggregate cost function. Each sensor can communicated to a few “neighbors”, furthermore, the communication channels have limited capacities. To solve the resulting optimization problem, we use a weighted modification of the distributed consensus-based SPSA algorithm whose main advantage over the alternative method is its ability to work in presence of arbitrary unknown-but-bounded noises whose statistical characteristics can be unknown. We provide a convergence analysis of the weighted SPSA-based consensus algorithm and show its efficiency via numerical simulations.

Original languageEnglish
Pages (from-to)126-131
Number of pages6
JournalIFAC-PapersOnLine
Volume54
Issue number7
DOIs
StatePublished - 1 Jul 2021
Event19th IFAC Symposium on System Identification, SYSID 2021 - Padova, Italy
Duration: 13 Jul 202116 Jul 2021

Scopus subject areas

  • Control and Systems Engineering

Keywords

  • Consensus
  • Distributed parameter estimation
  • Randomized algorithms
  • Sensor network

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