Inference and Learning of Nonlinear LFR State-Space Models

Merijn Floren; Jean-Philippe Noël; Jan Swevers

doi:10.1109/lcsys.2025.3580354

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arXiv · 2503.14409

Inference and Learning of Nonlinear LFR State-Space Models

Merijn Floren·Jean-Philippe Noël·Jan Swevers

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Abstract

Estimating the parameters of nonlinear block-oriented state-space models from input-output data typically involves solving a highly non-convex optimization problem, which is prone to poor local minima and slow convergence. This paper presents a computationally efficient initialization method for nonlinear linear fractional representation (NL-LFR) models using periodic data. By first inferring the latent signals and subsequently estimating the model parameters, the approach generates initial estimates for use in a later nonlinear optimization step. The proposed method shows robustness against poor local minima, and achieves a twofold error reduction compared to the state-of-the-art on a challenging benchmark dataset.

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Merijn Floren, Jean-Philippe Noël, Jan Swevers. 2025-03-18. Inference and Learning of Nonlinear LFR State-Space Models. https://doi.org/10.1109/lcsys.2025.3580354

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