Overview
- 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
Part of the book series: Studies in Computational Intelligence (SCI, volume 510)
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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.
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Keywords
Table of contents (7 chapters)
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Front Matter
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Back Matter
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)
Authors and Affiliations
Bibliographic Information
Book Title: Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis
Authors: Marcin Mrugalski
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-319-01547-7
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing Switzerland 2014
Hardcover ISBN: 978-3-319-01546-0Published: 19 August 2013
Softcover ISBN: 978-3-319-03286-3Published: 28 August 2015
eBook ISBN: 978-3-319-01547-7Published: 04 August 2013
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XXI, 182
Topics: Computational Intelligence, Artificial Intelligence, Complexity, Control and Systems Theory