| Titre : |
Learning algorithms based state estimation, optimization and control of nonlinear processes |
| Type de document : |
document électronique |
| Auteurs : |
Abdelhadi Abedou, Auteur ; Amine Rami Bennacer, Auteur ; Mohamed Tadjine, Directeur de thèse |
| Editeur : |
[S.l.] : [s.n.] |
| Année de publication : |
2024 |
| Importance : |
1 fichier PDF (7 Mo) |
| Présentation : |
ill. |
| Note générale : |
Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Automatique : Alger, École Nationale Polytechnique : 2024
Annexe p. 96 - 97 . - Bibliogr. p. 98 - 102 |
| Langues : |
Anglais (eng) |
| Mots-clés : |
Unmanned aerial vehicle
Icing
LMI
Neural networks
Sparse identification
IoT
Optimization |
| Index. décimale : |
PA00424 |
| Résumé : |
Machine learning (ML), including deep learning and reinforcement learning, offers powerful tools for addressing complex problems. This thesis leverages ML to enhance state estimation, system identification, and optimization in non-linear systems, where traditional methods often fall short. Key focus areas include improving accuracy in capturing complex system dynamics, extracting system characteristics directly from data, and solving non-convex problems. The thesis demonstrates these methods through applications in aircraft dynamics and smart sensor networks for IoT technologies, highlighting the potential of ML to enhance the performance, reliability, and adaptability of control systems. |
Learning algorithms based state estimation, optimization and control of nonlinear processes [document électronique] / Abdelhadi Abedou, Auteur ; Amine Rami Bennacer, Auteur ; Mohamed Tadjine, Directeur de thèse . - [S.l.] : [s.n.], 2024 . - 1 fichier PDF (7 Mo) : ill. Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Automatique : Alger, École Nationale Polytechnique : 2024
Annexe p. 96 - 97 . - Bibliogr. p. 98 - 102 Langues : Anglais ( eng)
| Mots-clés : |
Unmanned aerial vehicle
Icing
LMI
Neural networks
Sparse identification
IoT
Optimization |
| Index. décimale : |
PA00424 |
| Résumé : |
Machine learning (ML), including deep learning and reinforcement learning, offers powerful tools for addressing complex problems. This thesis leverages ML to enhance state estimation, system identification, and optimization in non-linear systems, where traditional methods often fall short. Key focus areas include improving accuracy in capturing complex system dynamics, extracting system characteristics directly from data, and solving non-convex problems. The thesis demonstrates these methods through applications in aircraft dynamics and smart sensor networks for IoT technologies, highlighting the potential of ML to enhance the performance, reliability, and adaptability of control systems. |
|
PA00100 
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