Data-driven recursive least squares methods for non-affined nonlinear discrete-time systems

DOI10.1016/j.apm.2020.01.040zbMath1481.93141OpenAlexW3002398753WikidataQ126300581 ScholiaQ126300581MaRDI QIDQ821735

Ronghu Chi, Biao Huang, Na Lin

Publication date: 21 September 2021

Published in: Applied Mathematical Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.apm.2020.01.040

zbMATH Keywords

recursive least squaresnonlinear nonaffine systemsdata-driven identificationdynamic linearization

Mathematics Subject Classification ID

Least squares and related methods for stochastic control systems (93E24) Identification in stochastic control theory (93E12)

Related Items (3)

Design of multi innovation fractional LMS algorithm for parameter estimation of input nonlinear control autoregressive systems ⋮ Decomposition‐based over‐parameterization forgetting factor stochastic gradient algorithm for Hammerstein‐Wiener nonlinear systems with non‐uniform sampling ⋮ Modeling nonlinear systems using the tensor network B‐spline and the multi‐innovation identification theory

Uses Software

• Daisy

Cites Work

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• Nonlinear black-box modeling in system identification: A unified overview
• On Model-Free Adaptive Control and Its Stability Analysis
• System Identification: New Paradigms, Challenges, and Opportunities

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