{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/inference-and-learning-of-nonlinear-lfr-state","title":"Inference and Learning of Nonlinear LFR State-Space Models","arxiv_id":"2503.14409","date":"2025-03-18","proceeding":null,"authors":["Merijn Floren","Jean-Philippe Noël","Jan Swevers"],"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.","url_abs":"https://arxiv.org/abs/2503.14409v2","url_pdf":"https://arxiv.org/pdf/2503.14409v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"inference-and-learning-of-nonlinear-lfr-state","repo_url":"https://github.com/merijnfloren/freq-statespace","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"jax","reach":null}],"tasks":[{"task_slug":"state-space-models","task_name":"State Space Models"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}