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Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis [electronic resource] / by Marcin Mrugalski



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Creatore: Mrugalski, Marcin. author Visualizza persona
Titolo: Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis [electronic resource] / by Marcin Mrugalski
Link to work: Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis Visualizza cluster
Pubblicazione: Cham : Springer International Publishing : Imprint : Springer, 2014
Estensione: 1 online resource.
Disciplina: 006.3
Titolo uniforme di collana: Studies in computational intelligence, 1860-949X ; 510
Index term-Uncontrolled: Computational Intelligence
Artificial Intelligence (incl. Robotics)
Complexity
Control
Engineering
Soggetto non controllato: Computational Intelligence
Artificial Intelligence (incl. Robotics)
Complexity
Control
Engineering
Termine d'indicizzazione-Occupazione: Computational Intelligence
Artificial Intelligence (incl. Robotics)
Complexity
Control
Engineering
Nota di contenuto: Introduction -- Designing of dynamic neural networks -- Estimation methods in training of ANNs for robust fault diagnosis -- MLP in robust fault detection of static non-linear systems -- GMDH networks in robust fault detection of dynamic non-linear systems -- State-space GMDH networks for actuator robust FDI.
Sommario/riassunto: The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems. A part of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault diagnosis schemes as well as the importance of robustness. The book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. The book is also devoted to advanced schemes of description of neural model uncertainty. In particular, the methods of computation of neural networks uncertainty with robust parameter estimation are presented. Moreover, a novel approach for system identification with the state-space GMDH neural network is delivered. All the concepts described in this book are illustrated by both simple academic illustrative examples and practical applications.
Collana: Studies in Computational Intelligence, 1860-949X ; 510
ISBN: 9783319015477
Formato: Risorse elettroniche
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9805691
Localizzazioni e accesso elettronico http://dx.doi.org/10.1007/978-3-319-01547-7
Lo trovi qui: University of Chicago
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Altra ed. diverso supporto: Printed edition: 9783319015460 Fa parte di: Springer eBooks