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arXiv · 2208.10124

Bilinear realization from input-output data with neural networks

Dimitrios S. Karachalios·Ion Victor Gosea·Kirandeep Kour·Athanasios C. Antoulas
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Abstract

We present a method that connects a well-established nonlinear (bilinear) identification method from time-domain data with neural network (NNs) advantages. The main challenge for fitting bilinear systems is the accurate recovery of the corresponding Markov parameters from the input and output measurements. Afterward, a realization algorithm similar to that proposed by Isidori can be employed. The novel step is that NNs are used here as a surrogate data simulator to construct input-output (i/o) data sequences. Then, classical realization theory is used to build a bilinear interpretable model that can further optimize engineering processes via robust simulations and control design.

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BibTeXRIS

Dimitrios S. Karachalios, Ion Victor Gosea, Kirandeep Kour, Athanasios C. Antoulas. 2022-08-22. Bilinear realization from input-output data with neural networks. https://arxiv.org/abs/2208.10124

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