.png){kind=icon}
返回
Learning nonlinear state-space models using autoencoders
.png){kind=icon}
PRE
AI
DOI：10.1016/j.automatica.2021.109666.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We propose a methodology for the identification of nonlinear state-space models from input/output data using machine-learning techniques based on autoencoders and neural networks. Our framework simultaneously identifies the nonlinear output and state-update maps of the model. After formulating the approach and providing guidelines for tuning the related hyper-parameters (including the model order), we show its capability in fitting nonlinear models on different nonlinear system identification benchmarks. Performance is assessed in terms of open-loop prediction on test data and of controlling the system via nonlinear model predictive control (MPC) based on the identified nonlinear state-space model. (C) 2021 Elsevier Ltd. All rights reserved.
Keyword:
Identification methods
Model fitting
Identification for control
Neural networks
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.9
论文数:
1.2W
被引数:
5.2W
机构
引用论文
.png){kind=icon}
Identification of metal ion solutions using acoustic plate mode devices and pattern recognition
PREAI
分享收藏
查看更多内容