Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis (Studies in Computational Intelligence, 510)
Mrugalski, Marcin
ISBN 10: 3319032860 ISBN 13: 9783319032863
Editorial: Springer, 2015
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Descripción
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SUPER FAST SHIPPING. N° de ref. del artículo 9783319032863
Sinopsis:
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.
De la contraportada:
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.
"Sobre este título" puede pertenecer a otra edición de este libro.
Detalles bibliográficos
Título: Advanced Neural Network-Based Computational ...
Editorial: Springer
Año de publicación: 2015
Encuadernación: Encuadernación de tapa blanda
Condición: New
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Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis
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ISBN 10: 3319032860
ISBN 13: 9783319032863
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemesDetails neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems. Nº de ref. del artículo: 385702883
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Taschenbuch. Condición: Neu. Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis | Marcin Mrugalski | Taschenbuch | xxi | Englisch | 2015 | Springer | EAN 9783319032863 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Nº de ref. del artículo: 109582259
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Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis (Studies in Computational Intelligence, 510)
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Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis
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ISBN 10: 3319032860
ISBN 13: 9783319032863
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 208 pp. Englisch. Nº de ref. del artículo: 9783319032863
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Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis
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Springer International Publishing, Springer International Publishing, 2015
ISBN 10: 3319032860
ISBN 13: 9783319032863
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Nº de ref. del artículo: 9783319032863
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Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis
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Springer International Publishing Aug 2015, 2015
ISBN 10: 3319032860
ISBN 13: 9783319032863
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 208 pp. Englisch. Nº de ref. del artículo: 9783319032863
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Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis (Studies in Computational Intelligence, 510)
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Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis (Studies in Computational Intelligence)
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