{"ID":2875931,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.01461","arxiv_id":"2509.01461","title":"A constrained optimization approach to nonlinear system identification through simulation error minimization","abstract":"This paper introduces a novel approach to system identification for nonlinear input-output models that minimizes the simulation error and frames the problem as a constrained optimization task. The proposed method addresses vanishing gradient issues, enabling faster convergence than traditional gradient-based techniques. We present an algorithm based on feedback linearization control of Lagrange multipliers and conduct a theoretical analysis of its performance. We prove that the algorithm converges to a local minimum, and it enhances computational efficiency by exploiting the problem's structure. Numerical experiments demonstrate that our approach outperforms gradient-based methods in both computational effort and estimation accuracy.","short_abstract":"This paper introduces a novel approach to system identification for nonlinear input-output models that minimizes the simulation error and frames the problem as a constrained optimization task. The proposed method addresses vanishing gradient issues, enabling faster convergence than traditional gradient-based techniques...","url_abs":"https://arxiv.org/abs/2509.01461","url_pdf":"https://arxiv.org/pdf/2509.01461v2","authors":"[\"Vito Cerone\",\"Sophie M. Fosson\",\"Simone Pirrera\",\"Diego Regruto\"]","published":"2025-09-01T13:26:35Z","proceeding":"math.OC","tasks":"[\"math.OC\",\"eess.SY\"]","methods":"[]","has_code":false}