https://github.com/biaslab/acc2022-vmpnarmax

Experiments and derivations for ACC2022 paper on variational message passing for online NARMAX identification.

https://github.com/biaslab/acc2022-vmpnarmax

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Keywords

free-energy-principle narmax simulation system-identification variational-message-passing

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factor-graph
Last synced: 6 months ago · JSON representation

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Experiments and derivations for ACC2022 paper on variational message passing for online NARMAX identification.

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  • Host: GitHub
  • Owner: biaslab
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: main
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  • Size: 26.7 MB
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free-energy-principle narmax simulation system-identification variational-message-passing
Created almost 5 years ago · Last pushed almost 4 years ago
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README.md

ACC2022 VMP-NARMAX

Experiments and supplementary derivations for the paper entitled

"Variational message passing for online polynomial NARMAX identification"

published at the 2022 American Control Conference.

The goal is infer parameters in a polynomial NARMAX model (see ForneyLab node code) and simulate outputs. Typically, (recursive) least-squares or another form of maximum-likelihood estimation is used. In this project, we employ variational Free Energy Minimisation in the form of variational message passing on a Forney-style factor graph.

We run a series of verification experiments on data generated from a NARMAX system, comparing performance as a function of sample size, polynomial order and simulation noise.

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  • Name: BIASlab
  • Login: biaslab
  • Kind: organization
  • Email: info@biaslab.org
  • Location: Eindhoven, the Netherlands

Bayesian Intelligent Autonomous Systems lab

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