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Online System Identification in a Duffing Oscillator by Free Energy Minimisation

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Active Inference (IWAI 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1326))

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Abstract

Online system identification is the estimation of parameters of a dynamical system, such as mass or friction coefficients, for each measurement of the input and output signals. Here, the nonlinear stochastic differential equation of a Duffing oscillator is cast to a generative model and dynamical parameters are inferred using variational message passing on a factor graph of the model. The approach is validated with an experiment on data from an electronic implementation of a Duffing oscillator. The proposed inference procedure performs as well as offline prediction error minimisation in a state-of-the-art nonlinear model.

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Notes

  1. 1.

    http://nonlinearbenchmark.org/.

  2. 2.

    Derivations at https://github.com/biaslab/IWAI2020-onlinesysid.

  3. 3.

    Experiment notebooks at https://github.com/biaslab/IWAI2020-onlinesysid.

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Acknowledgements

The author thanks Magnus Koudahl, Albert Podusenko and Thijs van de Laar for insightful discussions and the reviewers for their constructive feedback.

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Correspondence to Wouter M. Kouw .

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Kouw, W.M. (2020). Online System Identification in a Duffing Oscillator by Free Energy Minimisation. In: Verbelen, T., Lanillos, P., Buckley, C.L., De Boom, C. (eds) Active Inference. IWAI 2020. Communications in Computer and Information Science, vol 1326. Springer, Cham. https://doi.org/10.1007/978-3-030-64919-7_6

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  • DOI: https://doi.org/10.1007/978-3-030-64919-7_6

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