Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis: 510 - Rilegato

Mrugalski, Marcin

Rilegato

ISBN 10: 331901546X ISBN 13: 9783319015460

Casa editrice: Springer-Nature New York Inc, 2013

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.

Su questo libro

Dalla quarta di copertina

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.

Dettagli dell'edizione

• Editore: Springer-Nature New York Inc
• Data di pubblicazione: 2013
• Lingua: Inglese
• ISBN 10: 331901546X
• ISBN 13: 9783319015460
• Rilegatura: Copertina rigida
• Numero di pagine: 200
• Contatto del produttore: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Germania