Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis

  • Marcin Mrugalski

Part of the Studies in Computational Intelligence book series (SCI, volume 510)

Table of contents

  1. Front Matter
    Pages 1-17
  2. Marcin Mrugalski
    Pages 1-7
  3. Marcin Mrugalski
    Pages 9-46
  4. Marcin Mrugalski
    Pages 125-163
  5. Marcin Mrugalski
    Pages 165-167
  6. Back Matter
    Pages 169-181

About this book

Introduction

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.

 

Keywords

Computational Intelligence Fault Diagnosis Schemes Neural Networks Nonlinear Dynamic Systems Robustness

Authors and affiliations

  • Marcin Mrugalski
    • 1
  1. 1.Institute of Control and Computation EngineeringUniversity of Zielona GóraZielona GóraPoland

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-01547-7
  • Copyright Information Springer International Publishing Switzerland 2014
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-01546-0
  • Online ISBN 978-3-319-01547-7
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • About this book
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