Marcin Mrugalski

Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Springer-Verlag GmbH
• Seitenzahl: 182
• Erscheinungstermin: 4. August 2013
• Englisch
• ISBN-13: 9783319015477
• Artikelnr.: 44133185

Inhaltsangabe

Introduction.- Designing of dynamic neural networks.- Estimation methods in training of ANNs for robust fault diagnosis.- MLP in robust fault detection of static non-linear systems.- GMDH networks in robust fault detection of dynamic non-linear systems.- State-space GMDH networks for actuator robust FDI.

Rezensionen

From the reviews:

"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)