Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis / Studies in Computational Intelligence Bd.510 (PDF)
artificial neural networks to robust fault diagnosis schemes. It
presents neural networks-based modelling and estimation techniques used
for designing robust fault diagnosis...
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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.
- Autor: Marcin Mrugalski
- 2013, 2014, 182 Seiten, Englisch
- Verlag: Springer-Verlag GmbH
- ISBN-10: 3319015478
- ISBN-13: 9783319015477
- Erscheinungsdatum: 04.08.2013
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
- Dateiformat: PDF
- Größe: 4.91 MB
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- Vorlesefunktion
“The book deals with the use of artificial neural networks in robust fault diagnosis … . The ideas presented throughout the book are accompanied by examples and concrete applications. The book is devoted both to beginners in the field of fault diagnosis and advanced researchers in ANN model uncertainty.” (Smaranda Belciug, zbMATH, Vol. 1280, 2014)
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