Actions for Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis [electronic resource]

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Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis [electronic resource] / by Marcin Mrugalski

Author
Mrugalski, Marcin
Published
Cham : Springer International Publishing : Imprint: Springer, 2014.
Physical Description
XXI, 182 pages 125 illustrations : online resource
Additional Creators
SpringerLink (Online service)
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Series
Contents
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.
Summary
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.  
Subject(s)
ISBN
9783319015477
Digital File Characteristics
text file PDF
Note
AVAILABLE ONLINE TO AUTHORIZED PSU USERS.
Part Of
Springer eBooks
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