On the Convergence of DEM’s Linear Parameter Estimator

Ajith Anil Meera*, Martijn Wisse

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

The free energy principle from neuroscience provides an efficient data-driven framework called the Dynamic Expectation Maximization (DEM), to learn the generative model in the environment. DEM’s growing potential to be the brain-inspired learning algorithm for robots demands a mathematically rigorous analysis using the standard control system tools. Therefore, this paper derives the mathematical proof of convergence for its parameter estimator for linear state space systems, subjected to colored noise. We show that the free energy based parameter learning converges to a stable solution for linear systems. The paper concludes by providing a proof of concept through simulation for a wide range of spring damper systems.

Original languageEnglish
Title of host publicationMachine Learning and Principles and Practice of Knowledge Discovery in Databases
Subtitle of host publicationProceedings of the International Workshops of ECML PKDD 2021
EditorsMichael Kamp, Michael Kamp, Irena Koprinska, et. al.
PublisherSpringer
Pages692-700
ISBN (Print)978-3-030-93735-5
DOIs
Publication statusPublished - 2022
Event21st European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2021 - Virtual, Online
Duration: 13 Sep 202117 Sep 2021

Publication series

NameCommunications in Computer and Information Science
Volume1524 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference21st European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2021
CityVirtual, Online
Period13/09/2117/09/21

Keywords

  • Dynamic expectation maximization
  • Free energy principle
  • Linear state space systems
  • Parameter estimation

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