Parameter reduction in nonlinear state-space identification of hysteresis
Recent work on black-box polynomial nonlinear state-space modeling for hysteresis
identification has provided promising results, but struggles with a large number of …
identification has provided promising results, but struggles with a large number of …
Local model networks for the identification of nonlinear state space models
M Schüssler, T Münker, O Nelles - 2019 IEEE 58th Conference …, 2019 - ieeexplore.ieee.org
A novel algorithm for the identification of nonlinear state space models is proposed. The
local model state space network (LMSSN) uses local model networks for the approximation …
local model state space network (LMSSN) uses local model networks for the approximation …
Machine learning with nonlinear state space models
M Schüssler - at-Automatisierungstechnik, 2022 - degruyter.com
In this dissertation, a novel class of model structures and associated training algorithms for
building data-driven nonlinear state space models is developed. The new identification …
building data-driven nonlinear state space models is developed. The new identification …
Machine learning with finite impulse response models
T Münker - 2021 - dspace.ub.uni-siegen.de
An accurate and reliable model is the foundation for analysis, design, and control of a
modern automation system. It is often too complicated, too expensive, or too inaccurate to …
modern automation system. It is often too complicated, too expensive, or too inaccurate to …