{"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/recursive-nonlinear-system-identification","title":"Recursive nonlinear-system identification using latent variables","arxiv_id":"1606.04366","date":"2016-06-14","proceeding":null,"authors":["Per Mattsson","Dave Zachariah","Petre Stoica"],"abstract":"In this paper we develop a method for learning nonlinear systems with\nmultiple outputs and inputs. We begin by modelling the errors of a nominal\npredictor of the system using a latent variable framework. Then using the\nmaximum likelihood principle we derive a criterion for learning the model. The\nresulting optimization problem is tackled using a majorization-minimization\napproach. Finally, we develop a convex majorization technique and show that it\nenables a recursive identification method. The method learns parsimonious\npredictive models and is tested on both synthetic and real nonlinear systems.","url_abs":"http://arxiv.org/abs/1606.04366v3","url_pdf":"http://arxiv.org/pdf/1606.04366v3.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":"recursive-nonlinear-system-identification","repo_url":"https://github.com/magni84/lava","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}