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eess.SY2025
Inference and Learning of Nonlinear LFR State-Space Models
Merijn Floren, Jean-Philippe Noël, Jan Swevers
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…
eess.SY2020★ 27 cited
Experimental assessment of polynomial nonlinear state-space and nonlinear-mode models for near-resonant vibrations
Maren Scheel, Gleb Kleyman, Ali Tatar +5
In the present paper, two existing nonlinear system identification methodologies are used to identify data-driven models. The first methodology focuses on identifying the system us…