Towards lifelong learning of Recurrent Neural Networks for control design
Author:
Affiliation:
1. Politecnico di Milano,Dipartimento di Elettronica, Informazione e Bioingegneria,Milano,Italy,20133
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9837955/9837961/09838393.pdf?arnumber=9838393
Reference43 articles.
1. Autonomous Process Model Identification using Recurrent Neural Networks and Hyperparameter Optimization
2. Continual Learning Long Short Term Memory
3. Nonlinear MPC for Offset-Free Tracking of systems learned by GRU Neural Networks
4. Learning model predictive control with long short‐term memory networks
5. On the stability properties of Gated Recurrent Units neural networks
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1. Lifelong Learning for Monitoring and Adaptation of Data-Based Dynamical Models: A Statistical Process Control Approach;2024 European Control Conference (ECC);2024-06-25
2. Reconciling Deep Learning and Control Theory: Recurrent Neural Networks for Indirect Data-Driven Control;SpringerBriefs in Applied Sciences and Technology;2024
3. Physics-informed online learning of gray-box models by moving horizon estimation;European Journal of Control;2023-11
4. Design of a Deep Neural Network-Based Integral Sliding Mode Control for Nonlinear Systems Under Fully Unknown Dynamics;IEEE Control Systems Letters;2023
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