Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis - Couverture souple

Mrugalski, Marcin

9783319032863: Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis

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ISBN 10 : 3319032860 ISBN 13 : 9783319032863

Editeur : Springer, 2015

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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.

Les informations fournies dans la section � Synopsis � peuvent faire r�f�rence � une autre �dition de ce titre.

�diteur: Springer
Date d'�dition: 2015
Langue: anglais
ISBN 10: 3319032860
ISBN 13: 9783319032863
Reliure: Broch�
Nombre de pages: 208
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Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis

Marcin Mrugalski

Edit� par Springer International Publishing, 2015

ISBN 10 : 3319032860 ISBN 13 : 9783319032863

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Etat : 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. N� de r�f. du vendeur 385702883

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Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis

Marcin Mrugalski

Edit� par Springer International Publishing, Springer International Publishing, 2015

ISBN 10 : 3319032860 ISBN 13 : 9783319032863

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Taschenbuch. Etat : 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. N� de r�f. du vendeur 9783319032863

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Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis

Marcin Mrugalski

Edit� par Springer International Publishing Aug 2015, 2015

ISBN 10 : 3319032860 ISBN 13 : 9783319032863

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Taschenbuch. Etat : 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. N� de r�f. du vendeur 9783319032863

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Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis (Studies in Computational Intelligence, 510)

Mrugalski, Marcin

Edit� par Springer, 2015

ISBN 10 : 3319032860 ISBN 13 : 9783319032863

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Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis

Marcin Mrugalski

Edit� par Springer International Publishing, Springer International Publishing Aug 2015, 2015

ISBN 10 : 3319032860 ISBN 13 : 9783319032863

Neuf Taschenbuch

Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Taschenbuch. Etat : Neu. 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. N� de r�f. du vendeur 9783319032863

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