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https://github.com/biaslab/iwai2020-onlinesysid

Code and experiments for IWAI 2020 submission on online system identification

https://github.com/biaslab/iwai2020-onlinesysid

Science Score: 26.0%

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    Low similarity (7.3%) to scientific vocabulary

Keywords

duffing-equation free-energy-principle system-identification
Last synced: 6 months ago · JSON representation

Repository

Code and experiments for IWAI 2020 submission on online system identification

Basic Info
  • Host: GitHub
  • Owner: biaslab
  • License: mit
  • Language: HTML
  • Default Branch: master
  • Homepage:
  • Size: 14 MB
Statistics
  • Stars: 1
  • Watchers: 3
  • Forks: 1
  • Open Issues: 0
  • Releases: 0
Topics
duffing-equation free-energy-principle system-identification
Created over 5 years ago · Last pushed over 5 years ago
Metadata Files
Readme License

README.md

IWAI2020 onlinesysid

Code and experiments for the paper entitled

"Online system identification in a Duffing oscillator by free energy minimisation" ,

presented at the International Workshop on Active Inference 2020.

Content

  • FEM_prederror.ipynb and FEM_simerror.ipynb are Jupyter notebooks containing the method described in the paper. The first is a 1-step ahead prediction error experiment and the other a simulation error experiment. It uses ForneyLab.jl and a custom node called "NLARX" provided here (NLARX-node folder). If you don't have Jupyter installed, you can read the notebook by opening FEM_prederror.html or FEM_simerror.html in a browser.

  • PEM_prederror.m and PEM_simerror.m are baseline methods implemented using Matlab's System Identification Toolbox. The trained model is stored in models/narx_sigmoidnet4.mat. Results can be loaded directly via results/results_narx_sigmoidnet4_ksteppred.mat or results/results_narx_sigmoidnet4_simulation.mat.

  • Data comes from the Nonlinear Benchmark, specifically the Silverbox problem.

Contact

Problems with running code or feedback on the method can be given in the issues tracker.

Owner

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

Bayesian Intelligent Autonomous Systems lab

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