Machine learning with nonlinear state space models

被引：1
|
作者：
Schuessler, Max [1 ]
机构：
[1] Univ Siegen, Inst Mech & Control Engn Mechatron, Paul Bonatz Str 9-11, D-57076 Siegen, Germany
来源：
AT-AUTOMATISIERUNGSTECHNIK | 2022年 / 70卷 / 11期
关键词：
system identification; neural networks; machine learning;
D O I：
10.1515/auto-2022-0089
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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 procedure with the resulting model is called local model state space network (LMSSN). Furthermore, recurrent neural networks (RNNs) and their similarities to nonlinear state space models are elaborated on. The overall outstanding performance of the LMSSN is demonstrated on various applications.
引用
收藏
页码：1027 / 1028
页数：2
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