Titre :

Combining neural networks and control: potentialities, patterns and perspectives

Auteur(s) :

Cerf, Sophie [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Rutten, Eric [Auteur]
Laboratoire d'Informatique de Grenoble [LIG]
Control for Autonomic computing systems [CTRL-A]

Titre de la manifestation scientifique :

IFAC 2023 - 22nd World Congress of the International Federation of Automatic Control

Organisateur(s) de la manifestation scientifique :

International Federation of Automatic Control

Ville :

Yokohama

Pays :

Japon

Date de début de la manifestation scientifique :

2023-07-09

Titre de l’ouvrage :

Proceedings of The 22nd World Congress of the International Federation of Automatic Control

Date de publication :

2023-07

Mot(s)-clé(s) en anglais :

Machine Learning
Neural Networks
Learning for control
Reinforcement learning and deep learning in control
Machine learning in modelling
prediction
control and automation
Data-driven control

Discipline(s) HAL :

Sciences de l'ingénieur [physics]/Automatique / Robotique
Informatique [cs]/Intelligence artificielle [cs.AI]

Résumé en anglais : [en]

Machine learning tools are widely used for knowledge extraction, modeling, and decision tasks; a range of problems that Control Theory also tackles. Their relations have been largely explored by looking at stochastic control ...
Lire la suite >Machine learning tools are widely used for knowledge extraction, modeling, and decision tasks; a range of problems that Control Theory also tackles. Their relations have been largely explored by looking at stochastic control and Markov Decision Processes, due to the proximity of their formulations. However, novel links between machine learning and deterministic control are emerging; combining both approaches, e.g. by performing identification with learning, or controlling the training process. The recent flourishing literature is vast: there is a need to identify challenges, trends and opportunities on this interface. This survey contributes i) to the compared analysis of both fields. ii) Based on literature review, a categorization of combinations of learning and control is drawn. In the control framework, learning has been used for modeling, controllers tuning or adaptation, generating a controller or as a controller itself, for translating complex objectives, or checking controlled systems. Conversely, in the learning framework, control is used for tuning hyperparameters, selecting or generating training data, as the training or decision-making algorithm itself or to guarantee learning properties. iii) Finally, discussions on the literature open novel promising combinations to be explored, such as control of neural networks' training process.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL