Learning Reduced Nonlinear State-Space Models: an Output-Error Based Canonical Approach-Reference-Cited by-同舟云学术

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Learning Reduced Nonlinear State-Space Models: an Output-Error Based Canonical Approach

Published:2022-12-06 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE 61st Conference on Decision and Control (CDC)
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Author:

Janny Steeven¹,Possamai Quentin¹,Bako Laurent²,Wolf Christian³,Nadri Madiha⁴

Affiliation:

1. INSA-Lyon,LIRIS UMR CNRS 5205

2. Univ Lyon,Ecole Centrale de Lyon, CNRS, Ampère,Ecully,France,69130

3. Naver Labs Europe,Meylan,France

4. Université Claude Bernard Lyon 1,LAGEPP

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9992315/9992317/09993232.pdf?arnumber=9993232

Reference26 articles.

1. Blackbird Dataset: A large-scale dataset for UAV perception in aggressive flight;Antonini;Robotics Research,2018

2. NeuroBEM: Hybrid Aerodynamic Quadrotor Model

3. Nonlinear state-space identification using deep encoder networks;Beintema

4. Nonlinear state-space identification using deep encoder networks;Beintema;Learning for Dynamics and Control,2021

5. Deep learning for universal linear embeddings of nonlinear dynamics

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Data-Driven Control of Nonlinear Systems from Input-Output Data;2023 62nd IEEE Conference on Decision and Control (CDC);2023-12-13

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