https://github.com/biaslab/nsi-silverbox
Online system identification in Silverbox by minimising free energy
Science Score: 26.0%
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Low similarity (6.6%) to scientific vocabulary
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Online system identification in Silverbox by minimising free energy
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README.md
Online Nonlinear System Identification
The goal is infer parameters in a model that can predict future output, given new input. We are testing the following models:
- Nonlinear AutoRegressive model with eXogenous input (NARX; see ForneyLab node code)
- Nonlinear AutoRegressive Moving Average model with eXogenous input (NARMAX; see ForneyLab node code)
These models are standard in the control systems community. Typically, (recursive) least-squares or some other form of frequentist estimation is used. In this repo, we employ Free Energy Minimisation.
We run a series of verification experiments and a validation experiment on the Silverbox data set from the Nonlinear Benchmark.
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Owner
- Name: BIASlab
- Login: biaslab
- Kind: organization
- Email: info@biaslab.org
- Location: Eindhoven, the Netherlands
- Website: http://biaslab.org
- Repositories: 47
- Profile: https://github.com/biaslab
Bayesian Intelligent Autonomous Systems lab
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