Studies in Computational Intelligence

Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis

Authors: Mrugalski, Marcin

  • Devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes
  • Details neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems
  • Treats 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
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About this book

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.

 

Reviews

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)

Table of contents (7 chapters)

  • Introduction

    Mrugalski, Marcin

    Pages 1-7

  • Designing of Dynamic Neural Networks

    Mrugalski, Marcin

    Pages 9-46

  • Estimation Methods in Training of ANNs for Robust Fault Diagnosis

    Mrugalski, Marcin

    Pages 47-68

  • MLP in Robust Fault Detection of Static Non-linear Systems

    Mrugalski, Marcin

    Pages 69-92

  • GMDH Networks in Robust Fault Detection of Dynamic Non-linear Systems

    Mrugalski, Marcin

    Pages 93-124

Buy this book

eBook $99.00
price for USA (gross)
  • ISBN 978-3-319-01547-7
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $129.00
price for USA
  • ISBN 978-3-319-01546-0
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Rent the ebook  
  • Rental duration: 1 or 6 month
  • low-cost access
  • online reader with highlighting and note-making option
  • can be used across all devices
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Bibliographic Information

Bibliographic Information
Book Title
Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis
Authors
Series Title
Studies in Computational Intelligence
Series Volume
510
Copyright
2014
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing Switzerland
eBook ISBN
978-3-319-01547-7
DOI
10.1007/978-3-319-01547-7
Hardcover ISBN
978-3-319-01546-0
Series ISSN
1860-949X
Edition Number
1
Number of Pages
XXI, 182
Topics