Inference and Learning of Nonlinear LFR State-Space Models
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911040518750208 |
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| author | Floren, Merijn Noël, Jean-Philippe Swevers, Jan |
| author_facet | Floren, Merijn Noël, Jean-Philippe Swevers, Jan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_14409 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Inference and Learning of Nonlinear LFR State-Space Models Floren, Merijn Noël, Jean-Philippe Swevers, Jan Systems and Control 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. |
| title | Inference and Learning of Nonlinear LFR State-Space Models |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2503.14409 |