Advanced Neural Network-based Computational Schemes for Robust Fault Diagnosis
Language: English
Published by Springer, 2015
- Softcover
- Used
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- Title
- Advanced Neural Network-based Computational Schemes for Robust Fault Diagnosis
- Author
- Mrugalski, Marcin
- Publisher
- Springer
- Publication year
- 2015
- Condition
- As New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 3319032860
- ISBN 13
- 9783319032863
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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
GreatBookPrices
Columbia, MD, U.S.A.
AbeBooks seller since April 6, 2009
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